1996 soccer game in the Midwest, (Rick Dikeman image)

Nouméa ground station after the flood

A pencil and a coffee cup show the size of NASA’s teeny tiny PhoneSat

Bonus Hotspot: Naro Tartaruga AUV

Pacific lamprey spawning (photo by Jeremy Monroe, Fresh Waters Illustrated)

“Return of the Bucentaurn to the Molo on Ascension Day”, by (Giovanni Antonio Canal) Canaletto

The U.S. Naval Observatory Alternate Master Clock at 2nd Space Operations Squadron, Schriever AFB in Colorado. This photo was taken in January, 2006 during the addition of a leap second. The USNO master clocks control GPS timing. They are accurate to within one second every 20 million years (Satellites are so picky! Humans, on the other hand, just want to know if we’re too late for lunch) USAF photo by A1C Jason Ridder.

Detail of Compass/ BeiDou2 system diagram

Hotspot 6: Beluga A300 600ST

1. Mapping Air Traffic, Rainy Seasons, and More
Sahel, Africa

1. Mapping Air Traffic, Rainy Seasons, and More
Sahel, Africa
√ The European Space Agency (ESA) is using its Proba-V minisatellite to reveal – among other things – the seasonal changes in Africa’s sub-Saharan Sahel, with the rainy season allowing vegetation to blossom between February (top image) and September (bottom image). The semi-arid Sahel stretches more than 5,000 kilometers across Africa, from the Atlantic Ocean (Senegal, Mauritania) to the Red Sea (Sudan). The few months of the rainy season in the Sahel are much needed in these hot and sunny parts of Africa, and are critical for the food security and livelihood of their inhabitants.

Previously, the German Aerospace Center (DLR) and Luxembourg’s SES company added an experiment with Proba-V to detect Automatic Dependent Surveillance Broadcast (ADS-B) aircraft signals from space. These signals are regularly broadcast from aircraft, giving flight information such as speed, position and altitude.

Described as ESA’s – and the world’s – first precision formation flying mission, Proba-3 is currently used for a wide array of missions.

2. Educating GNSS Students
Indian state of Telangana
√ The establishment of a new JNTU-Hyderabad GNSS lab is designed to provide an opportunity to the students, scholars and faculty members to carry out research in satellite-based navigation and to develop several advanced applications.

The Jawaharlal Nehru Technological University-Hyderabad (JNTU-H) and Hexagon Capability Centre India (HCCI) established the GNSS laboratory at the Centre for Spatial Information Technology, JNTU-H, according to recent reports from Telangana.

The lab is equipped with NovAtel GNSS receivers, antenna, systems, cables and other hardware components. The equipment enables reception, processing, analysis and development of navigational data and applications to augment curriculum for JNTU-H students for research and education. The university is located in Kukatpally, Hyderabad, in the Indian state of Telangana.

3. Flying Fruit
Eastern China
√ Chinese e-commerce giant Alibaba announced that it has used drones to deliver packages over water for the first time. Three unmanned aerial vehicles (UAVs) carrying six boxes of passionfruit with a combined weight of around 12 kilograms flew from Putian in China’s eastern Fujian Province to nearby Meizhou Island on October 31, the company said in a statement.

Flying into a strong wind, the drones took nine minutes to make the five-kilometer crossing. Each drone can carry up to seven kilograms, according to state-run Xinhua news agency. The drones were jointly developed by Alibaba’s delivery arm Cainiao Network, the company’s rural shopping platform Rural Taobao, and a domestic technology firm. According to Zeng Jinmei, an online store owner based on the island, the drone delivery service will cut the transportation time in half.

Alibaba plans to use drones to deliver high value-added products such as fresh food and medical supplies over water in the future.

The post GNSS Hotspots | November 2017 appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

There are many good reasons for getting excited about highly automated vehicles, or HAVs, which is the acronym used by the National Highway Traffic Safety Administration (NHTSA). HAVs can make driving more fuel- and time-efficient. They can significantly reduce traffic congestion and emissions by driving a precise speed, minimizing lane changes, and maintaining an exact distance to neighboring cars. They can also increase accessibility and mobility for disabled and elderly persons.

There are many good reasons for getting excited about highly automated vehicles, or HAVs, which is the acronym used by the National Highway Traffic Safety Administration (NHTSA). HAVs can make driving more fuel- and time-efficient. They can significantly reduce traffic congestion and emissions by driving a precise speed, minimizing lane changes, and maintaining an exact distance to neighboring cars. They can also increase accessibility and mobility for disabled and elderly persons.

Sharing an HAV instead of owning is projected to dramatically reduce a household’s yearly transportation budget, which currently ranges between approximately $8,000 and $11,000 per car. HAVs carry promises not only in improved road mobility, and accessibility, but also in producing architectural and societal changes that can make mass parking spaces and personal car ownership obsolete in urban areas. Above all, HAVs can help improve road safety by preventing car accidents that cause more than 30,000 deaths/year in the United States alone, cost approximately $230 billion/year in medical and work loss costs, and are caused by humans 90% of the time.

Press articles in the 1950s and 1960s predicted that autonomous cars and “electronic highways” would become widely available by 1975. Major milestones in the use of new sensor, computation, and communication technology have recently reenergized the eagerness for HAVs. This first started with the 2005 “DARPA Grand Challenge”, where four different HAVs designed by teams of engineers from industry and academia completed a 132-mile trip across the Mohave desert in less than 7.5 hours with no human intervention. The 2007 DARPA “Urban Challenge” saw six teams autonomously complete a 60-mile course in an urban environment, while following traffic laws. Most teams used a combination of LiDAR, cameras, differential GPS, and computation power that is multiple orders of magnitude higher than what is typically needed for a commercial passenger vehicle. In 2009, Google (now Waymo) began designing and testing “self-driving” cars, which have since accumulated more than three million miles in autonomous mode.

Currently, most car manufacturers have HAV prototype systems and Google, Uber, NuTonomy have HAV pilot testing programs, including fully autonomous systems for public transportation, which, for now, are confined to segregated lanes and geo-fenced areas. Multiple Tier-2 supplier companies have emerged, which specialize in autonomous car technology. In early 2017, 36 companies were registered to test prototype HAV systems on public roads in the state of California.

However, in Figure 1 (for all figures, see inset photo, above right), Gartner’s “2016 Hype Cycle for Emerging Technologies” shows that HAV technology might be at the “peak of inflated expectations”, approaching the “trough of disillusionment”. Hype cycle curves are non-scientific tools that have been empirically verified for multiple example technologies over many years. Two example emerging technologies, commercial unmanned aircraft systems (UAS) and virtual reality, are included in Figure 1 for illustration purposes. The curve’s time scale may differ for each technology. One of many indicators of decreasing expectations on HAVs include a reduction in press coverage and the emergence of first negative news stories, in particular following the May 2016 crash of a Tesla Model S whose autopilot failed to distinguish a white trailer truck from the bright Florida sky. The Model S ran under the trailer causing its roof to be torn off and the operator to lose its life. The car kept going full speed on the side of the road through two fences until it hit a pole and came to a stop.

In parallel, until the end of 2016, Google was providing detailed reports of their self-driving car performance, which were designed to operate in real-world urban environments. These reports contain records of millions of miles driven autonomously, but also acknowledge “disengagements”, i.e., where the operator needed to take over control to avoid collisions. The data shows that HAVs are much more likely to be involved in collisions, even though these collisions are often of lower severity than in conventional human driving [HAVs typically get rear-ended because of their unusual road behavior] (see B. Schoettle, and M. Sivak, “A Preliminary Analysis of Real-World Crashes Involving Self-Driving Vehicles,” Additional Resources). Also, Uber’s autonomous taxis in Pittsburg have a reported rate of one disengagement per mile autonomously driven.

Moreover, the first fielded autonomous systems have revealed new safety threats. In particular, the technology’s functionality, as perceived by the human operator, does not always match the intended operational domain: for example, there have been cases of highway autopilots being used in urban areas and passing red lights without slowing down. In addition, human-machine interaction is at the heart of role confusion (is the operator or the HAV in charge?) of mode confusion (is the HAV in autonomous or manual mode?) and of the operator’s trust in this multimodal system. Misinterpretation may grow even wilder because a given functionality will not achieve the same level of performance across models and manufacturers, and operators may not be aware of the systems’ independently verified safety ratings. And, within the next few years, operators will be expected to anticipate hazardous situations and take over control. Thus, operating an HAV may require more education and different training than driving a car manually.

Current Safety Assessment Efforts
To focus this article, first consider the Society of Automotive Engineer (SAE) International’s classification of driving autonomy levels in Table 1 (see inset photo, above right). Under Levels 0 to 2, the human driver is responsible at all times, either for driving by himself, or for supervising the HAV in autonomous mode and taking control if needed. Under Levels 3 to 5, the system is self-monitoring and the driver is expected to take control, but only if requested by the system. Levels 0-4 provide partial automation under predefined driving modes and circumstances, whereas Level 5 is full autonomy.

The most advanced private car systems are currently Level 2, and pilot programs aim at achieving Level 3, although the mere presence of a kill-switch would imply that the system is actually Level 2. The transition from Level 2 to 3 is a remarkable leap that has significant implications on trust and comfort of human-machine interactions, on legal responsibility allocation between system and driver, and on technical challenges to overcome to guarantee passenger safety.

Over the past four years, the most publicized approaches to demonstrate Level 2 HAV safety have been experimental testing campaigns by Google, Tesla and Uber. Google’s approach to have HAVs drive millions of miles with minimal human intervention has been documented up until 2015. At this time, Google cars have autonomously travelled an impressive three million miles. Tesla’s autopilot is reported to have driven more than 130 million miles – on highways only – before it caused a fatality in May 2016.

In parallel, NHTSA reports about 3,000 billion miles travelled each year on U.S. highways by human drivers, with 30,000 deaths caused by traffic accidents; this corresponds to about one fatality in traffic accidents per 100 million miles driven in the U.S. But, this number accounts for incidents on all roads, in all weather conditions, and for all vehicle ages and types. Thus, a purely experimental, complete proof that HAVs match the level of safety of human driving would take about 400 years at Google’s current testing rate (of approximately 250,000 test miles per year), and would still take many decades if the testing rate increased exponentially. This is assuming that no fatalities occur during that time, that no major HAV upgrade is performed, and that the testing environment is representative of all U.S. roads. Thus, while an experimental proof is conclusive, it is not practical. Other, analytical, methods must be employed to ensure HAV safety.

Research Challenges In HAV Navigation Safety
Multiple technical aspects developed over decades for automated flying could serve as starting points for automated driving systems. Figure 2 shows research areas with overlap between aircraft (in blue) and car (in yellow) applications. Figure 2 is not intended to give a comprehensive list of all aspects of automation, but instead, it shows example technical areas that can be addressed using similar methods in aviation and automotive applications (in the green area). For example:

• performance standards set for software, communication, and electronic equipment are already being compared for aircraft versus cars in the NHTSA report by Q. D. Van Eikema Hommes, Additional Resources.
• the design of aircraft cockpit has been continuously improved over the past few decades, especially for highly-automated Unmanned Air Systems (UAS) with a remote pilot “in-the-box”; few car manufacturers envision futuristic car interiors where humans do not participate in driving, but as long as human-machine interactions are needed, lessons learned in cockpit design to avoid information overload are key. 
• while Automatic Dependent Surveillance-Broadcast (ADS-B) will be mandatory on all aircraft by 2020, a petition for proposed rule making has been issued to mandate Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) by the same date. (ADS-B is a situational awareness system for collision avoidance, through which aircraft share their positions with Air Traffic Control and with other aircraft.) 
• GNSS/INS navigation systems, which are extensively used in safety-critical aircraft navigation, are also being investigated for HAVs.
• overall safety standards also have similarities for aircraft and HAVs, which are discussed again below.

The focus of this article is on navigation safety. In aviation navigation, safety is assessed in terms of integrity (as well as accuracy, continuity, and availability, which are not discussed for brevity). Integrity is a measure of trust in sensor information: integrity risk is the probability of undetected sensor errors causing unacceptably large positioning uncertainty (See RTCA Special Committee 159, “Minimum Aviation System Performance Standards for the Local Area Augmentation System (LAAS), Additional Resources”). This top-level quantifiable performance metric is sensor- and platform-independent, and can thus be used to set certifiable requirements on individual system components to achieve and prove an overall level of safety.

The multiple separate efforts towards achieving Levels 3-to-5 HAVs reveal a compelling lack of coordination towards a common, uniform, quantifiable safety goal. Integrity can be used as an objective performance metric for open, transparent comparison and categorization across manufacturers. It can also provide a governmental regulating agency performance and testing standards for HAV certification, which would help accelerate the development, growth, and maturation of such HAVs, as displayed in Figure 3.

Moreover, the Federal Aviation Administration (FAA) has developed analytical methods to evaluate integrity. This provides the means to:

• quantify safety of existing multi-sensor systems under a variety of operating environments, thereby reducing the need for experimental testing
• allocate safety requirements to individual system components to achieve an overall target level of safety, thereby enabling design for safety 
• perform risk prediction, which is a key operational feature to enable hazard avoidance maneuvers

Several methods have been established to predict the integrity risk in GNSS-based aviation applications, which are instrumental in ensuring the safety of pilots and crew. As an example, Figure 4 illustrates a simplified definition of the integrity risk for aircraft landing applications. The aircraft positioning prediction is uncertain because of sensor measurement noise. An alert limit (AL) requirement box is represented around the predicted aircraft position. This AL is set by the certification authority, i.e., by the FAA in this application. Simply put, the risk of the actual aircraft position being outside the AL box is the integrity risk. (In practice, the most challenging part of risk prediction is to account for potentially undetected sensor faults, such as excessive GNSS satellite clock drift.)

Unfortunately, the same methods do not directly apply to HAVs, because ground vehicles operate under sky-obstructed areas where GNSS signals can be altered or blocked by buildings and trees. In general, the HAV environment is much more unpredictable than the aircraft’s, for reasons that include:

• a changing environment: traffic lights, construction, impact of rain on road adherence, sensor masking and occlusions,
• environmental diversity: intersection topography, road conditions, markings on ground, various traffic signs 
• road users that may interfere with HAV motion: other cars, trucks, pedestrians, bicyclists, etc. 
• comparatively large number of car manufacturers, equipment suppliers, and vehicle models, as well as with shorter model cycles than aircraft, causing wide variations in vehicle age and maintenance levels 
• non-uniform vehicle and road regulations at both the state and federal levels in the U.S. coupled with different international standardization processes.

Thus, HAVs require sensors in addition to GNSS, including laser scanners, radars, cameras, and odometers.

The parallel between aircraft and car applications in Figure 4 illustrates the significant challenge that lies ahead when bringing aviation safety standards to HAVs. It took decades of research and considerable resources to bring the alert limit requirement box down to 10 meters above and below the aircraft using the FAA’s GPS augmentation systems (the Wide-Area Augmentation System and the Local Area Augmentation System). For a car to stay in its lane, the alert limit requirement box must be an order of magnitude smaller, and has to maintain this level of safety in a more dynamic and unpredictable environment.

HAV Taxonomy
Creating a path to successful automated navigation requires an overall methodology to prioritize on imminently achievable objectives, and then expand to more challenging missions. First in this HAV taxonomy, a classification using six SAE autonomy levels has been presented in Table 1. This classification is further refined by segmenting a car’s trip into basic driving competencies, and by specifying the conditions under which a given HAV shall achieve these competencies. A similar classification was made in the early days of GPS-based commercial aircraft navigation safety analysis, where distinctions were made between different phases of flight, weather conditions, vehicle equipment, and airport infrastructure capabilities.

For example, in the early 1990’s, 40% of aircraft accidents were occurring during final approach and landing, and 26% during take-off and initial climb, which only represented an average of 4% and 2% of flight time, respectively. The FAA therefore concentrated their efforts on improving safety during these phases of flight. GPS augmentation systems were designed, with varying capabilities depending on airborne equipment and airport infrastructure, to guide the aircraft under the cloud ceiling, or to bring it all the way to touch-down. Similarly, the “first and last mile” are identified as the most challenging parts of HAV operations, whereas highway auto-drive systems have already been developed and implemented. In its 2016 Federal Automated Vehicles Policy, NHTSA identifies 28 HAV behavioral competencies, which are particularly challenging to meet in the first and last miles of a typical trip. These competencies are basic abilities that an HAV must have to complete nominal driving tasks; they include, for example, lane keeping, obeying traffic laws, and responding to other road users.

To better describe an HAV’s ability, the Federal Automated Vehicles Policy further specifies that basic driving competencies should be available under an HAV’s predefined Operational Design Domain (ODD), described by its geographical location, road type and condition, weather and lighting condition, vehicle speed, etc. The ODD captures the circumstances under which an HAV is supposed to operate safely.

Such classification is key to safety analysis. It can allow HAVs at different stages of their development to be simultaneously fielded, and for them to evolve by expanding their ODDs. The classification can also help in identifying geographical areas where improved road infrastructure is needed for automated operation, similar to airports requiring equipment for instrument navigation to deal with higher traffic density.

Furthermore, standards for electronic equipment, measured by Automotive Safety Integrity Levels, have been issued and compared with the aviation’s Design Assurance Levels (DAL). And, overall system safety levels have been codified, which in aviation account for both the severity and probability of occurrence of an incident, and in automotive applications account, in addition, for “controllability”, which is a measure of how likely an average driver is to maneuver out of a given imminent danger.

All of the above elements: (a) HAV autonomy level, (b) basic driving competency, (c) operation design domain, (d) vehicle electronic equipment, and (e) overall safety risk requirement must be specified to carry out a formal HAV safety analysis. Still missing from the HAV documents are clear guidelines, or example methods, on how to implement these safety requirements.

A Path Towards HAV Navigation Safety
When quantifying the safety of HAV navigation systems, such as in the example displayed in Figure 5, every component of the system including raw sensors, estimator and integrity monitor, and safety predictor, can potentially introduce risk. Unlike aircraft, HAVs require multiple and varied sensors to compensate for GPS signal blockages caused by buildings and trees. These sensor types must be integrated, and new methods to evaluate the integrity of multi-sensor systems must be developed. Furthermore, HAVs must have the ability to continuously predict integrity in a dynamic HAV environment.

In general, research on analytical evaluation of HAV navigation safety is sparse. For example, J. Lee et alia, Additional Resources use the concept of a “safe driving envelope,” but the approach focuses mostly on collision avoidance. The paper by O. Le Marchand, et alia, evaluates ground vehicle navigation, but shows an “approximate radial-error” of tens of meters, far exceeding the necessary sub-meter alert limit. A multi-sensor augmented-GPS/IMU system is used in the paper by R. Toledo-Moreo, et alia with “horizontal trust levels” of 7 meters to 10 meters, still an order-of-magnitude higher than the required HAV alert limit.

Multi-sensor integrity is addressed by M. Brenner, Additional Resources, but for a sensor combination specific to aviation and insufficient for terrestrial mobile robots. Other approaches to multi-sensor integration show promise, but do not provide rigorous proof of integrity. In fact, most publications use pose estimation error covariance as a measure of performance, which is understood as not being sufficient, but is the only metric currently available. Most critically, the metric does not account for fault modes introduced by feature extraction and data association, two algorithms commonly used in mobile robot localization (and discussed again below).

Unlike GPS, which gives absolute position fixes, IMUs, LiDAR, radar, and cameras provide relative displacements with respect to a previous time-step, or with respect to a map. Thus, measurement time-filtering is required, which makes integrity risk evaluation more challenging since past-time sensor errors and undetected faults can now impact current-time safety.

Example LiDAR Navigation Safety Evaluation
While safety quantification for GNSS and GNSS/INS has been rigorously performed for aviation applications, and is being researched for HAVs, navigation safety for LiDAR, radar, camera, and multi-sensor navigation is a widely unexplored research area. To provide a specific example on the research work that lies ahead, we have started developing safety risk evaluation methods for LiDARs. We selected LiDARs because of their prevalence in HAVs, of their market availability, and because of our prior experience. However, the techniques we are developing are general enough that radar, cameras, or any future sensor that returns range data can be substituted.

Raw range data must be processed before it can be used for navigation. One technique, visual odometry, establishes correlations between successive scans to estimate sensor changes in pose (i.e., position and orientation). These processes are highly computationally intensive, and have the same problems as other dead-reckoning techniques, such as wheel odometry over time. Thus, they can become inaccurate or cumbersome for HAVs moving over multiple time epochs. Although proprietary information regarding the use of visual odometry by HAV manufacturers is unavailable, the research literature suggests that it is only used for short time scale operations. A second class of algorithms provides sensor localization by extracting static features from the raw sensor data and associating those features to a map. This is typically done in two steps, as illustrated in Figure 6: feature extraction (FE) and data association (DA). The resulting information can then be iteratively processed using sequential estimators (e.g., Extended Kalman filter or EKF), which has been readily used in many practical applications.

There are several problems that the FE and DA algorithms are addressing. First, landmarks in the environment are unidentified, and their observations are not tagged in a manner similar to a GNSS satellite signal’s Pseudo Random Noise (PRN) number. Thus, the feature extraction algorithm must isolate the few most consistently identifiable, viewpoint-invariant landmarks in the raw sensor data. These features must be identifiable over repeated observations and distinguishable from one landmark to another. Features that are difficult to distinguish from each other can be found easily, but the possibility that the association is incorrect will greatly negatively impact the integrity risk.

Second, range data based on extracted features must match those features with those from a feature database or map. Data association algorithms accomplish this; however, incorrect associations commonly occur. These can lead to large navigation errors, as illustrated in Figure 6, thereby representing a threat to navigation integrity.

FE and DA can be challenging in the presence of sensor uncertainty. This is why many sophisticated algorithms have been devised. But, how can we prove whether these FE and DA methods are safe for life-critical HAV navigation applications, and under what circumstances? These research questions are currently unanswered. The most relevant publications on DA risk are found in literature on multi-target tracking. For example, in the paper Y. Bar-Shalom and T. E. Fortmann, an innovation-based nearest-neighbor DA criterion is introduced, which serves as basis in many practical implementations. The article by Y. Bar-Shalom, et alia, “The Probabilistic Data Association Filter,” provides a detailed derivation of the probability of correct association given measurements. However, this Bayesian approach is not well suited for safety-critical applications due to the lack of risk prediction capability, and to the problem of bounding the a-posteriori probability of association (a similar issue is encountered in the paper by F.C. Chan, et alia. Another insightful approach is followed in the paper by J. Areta, et alia). However, it makes approximations that do not necessarily upper-bound risks, hence do not guarantee safe operation, and it presents exact solutions that can only be evaluated using computationally expensive numerical methods, not adequate for real-time navigation. Also, the risk of FE is not addressed.

In response, we have been developing a new, computationally-efficient integrity risk prediction method to ensure safety of localization using LiDAR-based FE and DA. We have derived a multiple-hypothesis innovation-based DA method that provides the means to predict the probability of incorrect associations considering all potential landmark permutations. (For more details on these methods, see the following four papers in Additional Resources, Nos. 31, 49, 50 and 51.) We also determined a probabilistic lower bound on the minimum feature separation, which is guaranteed at FE, with pre-defined integrity risk allocation. The separation bound can be incorporated in an overall integrity risk equation. This new method was analyzed and tested to quantify the impact of incorrect associations on integrity risk. It showed that the positioning error covariance can be a misleading safety performance metric since cases were found where the contributions of incorrect associations to integrity risk far surpassed that of nominal errors accounted for in the positioning error covariance. In addition, the following key safety-tradeoff was illustrated: the more measurements are extracted, the lower the integrity risk contribution is under the correct association hypothesis, but the higher the other integrity risk contributions become because the risk of incorrect associations increases in the presence of cluttered, poorly-distinguishable landmarks. Finally, being surrounded by many landmarks increases the probability of continuous, uninterrupted navigation. The next step of this research aims at dealing with unmapped and non-static obstacles, and at quantifying the continuity risk of FE and DA.

Conclusion
Looking at the emergence of future HAV technology with the prior experience of aircraft navigation safety provides the means to scale up the challenges that lie ahead in the development of fully autonomous (Level 4 and 5) driverless cars. Many parallels can already be drawn between aviation safety requirements and early HAV standards and regulations. Still, the methods to fulfill these standards and regulations have to be established. If analytical methods are pursued, the following tasks need to be accomplished: (1) establish high-integrity raw sensor measurement error and fault models for non-GPS sensors; (2) develop analytical methods to quantify the safety risk of feature extraction and data association algorithms required in LiDAR, radar, and other pre-processing steps in camera-based localization; (3) design multi-sensor pose estimators and integrity monitors to evaluate the impact of undetected sensor faults on safety risk; and (4) derive, analyze, and experimentally implement integrity risk prediction in dynamic environments.

If these challenges are overcome, one will be able to quantify and prove the performance of an HAV’s navigation system — an essential part of safety. Proving navigation system integrity will also help give humans more confidence to trust HAVs, thus further developing the symbiotic relationship between humans and co-robots. Finally, as HAV technology progresses from driver’s aids such as active brake assist to full autonomous driving, this research is relevant now and will remain essential throughout the evolution of HAV technology.

Additional Resources
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1996 soccer game in the Midwest, (Rick Dikeman image)

Nouméa ground station after the flood

A pencil and a coffee cup show the size of NASA’s teeny tiny PhoneSat

Bonus Hotspot: Naro Tartaruga AUV

Pacific lamprey spawning (photo by Jeremy Monroe, Fresh Waters Illustrated)

“Return of the Bucentaurn to the Molo on Ascension Day”, by (Giovanni Antonio Canal) Canaletto

The U.S. Naval Observatory Alternate Master Clock at 2nd Space Operations Squadron, Schriever AFB in Colorado. This photo was taken in January, 2006 during the addition of a leap second. The USNO master clocks control GPS timing. They are accurate to within one second every 20 million years (Satellites are so picky! Humans, on the other hand, just want to know if we’re too late for lunch) USAF photo by A1C Jason Ridder.

Detail of Compass/ BeiDou2 system diagram

Hotspot 6: Beluga A300 600ST

1. Mangrove Tree-Planting Drones
Myanmar (Southeast Asia)

1. Mangrove Tree-Planting Drones
Myanmar (Southeast Asia)
√ For about five years now, a group of villagers in the delta of the Irrawaddy River in Myanmar (also known as Burma) has painstakingly planted 2.7 million mangrove trees with the hopes of beginning to restore an ecosystem that has been disappearing for decades. But this work is rather laborious, and the local nonprofit guiding the work wants to cover a much larger area — so they’re turning drones to help with their large-scale tree-planting project.

The drones, from the startup BioCarbon Engineering, can plant as many as 100,000 trees in a single day, leaving the local community to focus on taking care of the young trees that have already started to grow, according to the company, which has offices in Oxford, U.K., Sydney, Australia and Dublin, Ireland. In September, the company will begin a drone-planting program in the area along with Worldview International Foundation, the nonprofit guiding local tree-planting projects. To date, the organization has worked with villagers to plant an area of 750 hectares, about twice the size of Central Park. The drones will help cover another 250 hectares with 1 million additional trees. Ultimately, the nonprofit hopes to use drones to help plant 1 billion trees in an even larger area.

In the past villages have spent years replanting mangroves along the Irrawaddy River. With drones, their work will now go much faster.

2. Laser-Mapping Landscape Changes
Gargoyle Ridge in the McMurdo Dry Valleys, Antarctica
√ With the help of LiDAR, researchers led by Portland State University (PSU) have publicly released high-resolution maps of Antarctica’s McMurdo Dry Valleys, a unique desert region. The high-resolution maps cover 3,564 square kilometers of the McMurdo Dry Valleys and allow researchers to compare present-day conditions with the last surveys conducted more than a decade ago.

The research project led by PSU, and funded by the United States National Science Foundation (NSF), mapped the area using LiDAR, a remote-sensing method that uses laser beam pulses to measure the distance from the detector to the Earth’s surface. The data, collected by aerial survey missions flown in the Southern Hemisphere summer of 2014-2015, provides detailed imagery of the perpetually ice-free region, where changes, such as rapid erosion along some streams, have been observed in recent years.

The LIDAR maps are publicly available on two NSF-funded facilities: Open Topography, and the Polar Geospatial Center.

The McMurdo Dry Valleys are interesting to a wide range of scientists from biologists to geologists to glaciologists. The valleys are, for example, one of the few places on the massive continent—which is the size of the U.S. and Mexico combined—where bedrock is exposed, allowing geologists to reconstruct the continent’s geological history.

The region also is home to one of NSF’s Long Term Ecological Research sites, which support studies of its unusual habitat, dominated by microbial life, both in the soil and in unique ecosystems under at least one of its glaciers and in several of its highly salty lakes.

Evidence of past glacial advance and retreat is also more easily observed in the Dry Valleys, which provides window into the past behavior of the vast Antarctic ice sheets, the activity of which can influence global sea levels.

3. Fries with Your Drone Delivery?
Reykjavik, Iceland
√ Impatient Icelanders are getting help from Flytrex, an Israeli startup, that just started delivering small orders like takeout food by drone in a partnership with Aha, Iceland’s largest instant delivery platform. The drones, technically hexacopters, were approved by the Icelandic Transport Authority to pick up orders from restaurants and stores on one side of Reykjavik, where Aha has its offices, and fly them to a drop-off point in the suburb of Grafarvogur.

While Flytrex and Aha don’t offer direct store-to-home-delivery, the companies said that even on a trial basis the service would slash waiting times in a city whose bay delivery trucks must skirt to reach their destinations. A drone cuts delivery times by flying across the water to a truck that will complete the delivery.

Flytrex doesn’t make drones but develops autonomous, drone-based delivery systems. The drones can carry packages weighing up to three kilograms, about the size of a mailbox, so they can only handle smaller orders or takeout food.

The single drone now in use can make between 20 and 60 flights day, according to Flytrex, which has developed hardware that is installed on the drone and links it to a cellular network via a SIM card that enables a controller to locate, monitor its speed, altitude and other parameters in real time.

4. Tough Testing for Galileo
Noordwijk, the Netherlands
√ Each Galileo satellite must go through a rigorous test campaign to assure its readiness for the violence of launch, airlessness and temperature extremes of Earth orbit. Each one is dispatched to a unique location in Europe to ensure its readiness prior to launch: a 3,000-square meter cleanroom complex nestled in sandy dunes along the Dutch coast, filled with test equipment to simulate all aspects of spaceflight.

The test centre in Noordwijk – Europe’s largest satellite test site – is part of ESA’s main technical center, but it is maintained and operated on a commercial basis on behalf of the Agency by a private company created for the purpose: European Test Services (ETS) B.V.

ETS has been responsible for supporting many historic test campaigns – including space-certifying Europe’s 20-metric-ton ATV space truck and Envisat, the world’s largest civilian Earth-observing mission. But in terms of scale alone, its work with Galileo is the company’s greatest challenge.

ETS is about to complete its contracts with OHB System AG, covering the environmental test of 22 “Full Operational Capability” Galileo satellites, preceded by the testing of the very first of the first-generation “In-Orbit Validation” Galileo satellites on a previous, separate contract.

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Q: How do you use GNSS to compute the attitude of an object?

A: GNSS technology is used to support a wide range of position, velocity and time applications across numerous platforms. One of the lesser-known applications of GNSS is its ability to determine the attitude, or orientation, of an object. Such systems can be made to be quite small and can yield accurate solutions that operate in virtually any environment in which GNSS satellite visibility is reasonable. The only requirement from a GNSS receiver perspective is that the receiver be able to provide reliable carrier phase measurements.

This article begins with a brief summary of how the attitude of a vehicle is determined and then explains how GNSS can be used. It wraps up with a brief discussion about attainable accuracies.

Attitude Determination
Attitude determination is the process of determining the rotation angles that relate two different coordinate frames. Although any two coordinate frames can be used, we herein consider the rotation between a local-level frame (e.g., North, East, Up or East, North, Down, etc.) and the body frame. The body frame is a frame attached to the object (“body”) whose attitude is desired (an example body frame is given later).

With this in mind, we present the following key relationship

r⃗^l = R^l_br⃗^b     (1) 

where r⃗ is a vector parameterized in the local-level (superscript l) or body (superscript b) frame and R^l_b is the rotation matrix (or direction cosine matrix) from the body frame to the local-level frame.

The rotation matrix between any two frames can be defined using three consecutive rotations about the three coordinates axes — these are called Euler angles. For the case under consideration, since one of the frames is the local-level frame, the Euler angles can be expressed as familiar roll, pitch and azimuth angles.

To compute the rotation matrix, one needs to know or measure at least two vectors in each coordinate frame. These vectors could be anything including velocity, rotation rates, etc., as long as they are non-collinear (i.e., not parallel). It is possible to use a single vector, but then you cannot determine all three Euler angles; more on this later.

GNSS Attitude System Setup
For GNSS attitude determination systems, the vectors used in equation (1) are relative position vectors. The question, of course, is: where do these come from?

Before answering this question, let’s clarify what constitutes a GNSS attitude system. To determine the full attitude, at least three GNSS receivers are needed. The only other requirement is some software to process the data from these receivers.

Returning to the question that opened this section, the body frame vectors are defined by the location of the receivers (actually the antennas; denoted A ‒ C) on the object whose attitude is desired. Assuming a three-antenna system, a common (if not easy to understand) configuration is to mount the antennas such that two antennas fall along the direction of travel, and the third one is 90 degrees offset. One possible setup is illustrated in Figure 1 (see inset photo, above right) for the case of a road vehicle.

Once installed, the body frame coordinates are defined by measuring the position of the antennas relative to the coordinate frame of the vehicle. The coordinate frame of the vehicle can be arbitrarily defined but is usually selected such that one axis is along the direction of travel, one lateral to the direction of travel, and the third axis completes on orthogonal frame (e.g., forward, right, down).

As might be expected, these same vectors in the local-level frame are determined from GNSS measurements. Once computed, the rotation matrix between the body and local-level frames can then be determined. Both of these steps are accomplished by the processing software and are discussed in the next section.

Data Processing
As mentioned, the inter-antenna vectors in the local-level frame are computed from GNSS. For an N-antenna system, only N-1 independent vectors need to be solved. Although not required, this is commonly done by selecting one antenna/receiver as the “base” and then computing the vector to each of the other antennas/receivers in the system.

Continuing with the three-receiver example in Figure 1, we select antenna A as the base and compute vectors ray AB and ray BC. Of course, if there are other antennas, the vector from Point A to each point would also be computed.

To be more specific, the inter-receiver vectors are computed using standard differential carrier phase processing. Because the inter-antenna spacing is typically limited to a few meters, the spatially-correlated orbit, ionosphere and troposphere errors are virtually zero. This dramatically simplifies the ambiguity resolution process, since the only errors that need to be handled are multipath, noise and antenna phase center variation, which are generally small, as discussed later.

It is also possible to use the known baseline length between the receivers (as measured in the body frame), or any a priori attitude information to make the ambiguity resolution process even more robust.

Some of you might be wondering why I have not mentioned the requirement for a base station. The reason is precisely because we are estimating relative, not absolute, position vectors. By definition, differential data processing yields a relative position vector. If the location of the base station is known in an absolute sense — this is the most common use of a base station — then the resulting solution of the rover is also absolute.

For attitude determination, absolute location is not important. As such, the location of the base receiver (Point A in the above example) can be computed from a standalone (single point) solution. Even absolute positioning errors of 100 meters (which would be extremely large if the carrier phase data needed for attitude determination is still available) will be buried by the other errors in the system, and thus can be ignored.

The only other effect that might be important here is the timing accuracy of each receiver. As discussed in the March/April 2011 GNSS Solutions column, “GNSS Receiver Clocks,” a relative timing error of Δt between the receivers will result in a relative position error of s · Δt, where s is the speed of the vehicle. Assuming a timing error of 2 milliseconds (most receivers limit timing errors to ±1 milliseconds) and a vehicle traveling at 100 kilometers per hour, the relative positioning error would be approximately 5.6 centimeters. As discussed below, this would dominate the error budget and would therefore have to be properly accounted for.

The outputs of the GNSS processing are the vectors in the local-level frame; these can be computed directly in that frame or can be computed from vectors in an Earth-Centered Earth-Fixed (ECEF) frame. Mathematically, this is written as

r⃗^GNSS = r⃗^l + n⃗^GNSS
= R^l_br⃗^b + n⃗^GNSS     (2)

where r⃗^GNSS is the GNSS-derived vector in the local-level frame, n⃗^GNSS is the measurement noise, and equation (1) was used to get from the first to second line. Equation (2) is written in parametric form and can thus be used as input to any standard least-squares or Kalman filtering estimation algorithm to estimate the Euler angles embedded in R^l_b.

Two-Antenna Systems
Until now, we have only considered systems consisting of three or more receivers in order to estimate the full attitude of an object. However, two-receiver systems can be used to estimate rotations orthogonal to the vector connecting the two antennas.

The most common two-receiver system is one where the two receivers are set up parallel (or orthogonal) to the direction of travel. This allows for estimation of the azimuth and pitch (or roll) of the vehicle; the third angle is unobservable.

Expected Accuracy
As discussed above, the GNSS measurement errors are limited to multipath (2‒3 centimeters), noise (less than 1 millimeter) and phase center variation (1‒2 centimeters or less) (all values quoted as one standard deviation values). Assuming the measurement geometry is reasonable (HDOP ≈ 1 and VDOP ≈ 1.5), the main factor affecting accuracy is the length of the inter-antenna vectors.

To illustrate, let’s consider a two-receiver system with the understanding that there is an analogous relationship for three-plus receiver systems. Specifically, for the setup in Figure 2 (see inset photo, above right), for an inter-receiver separation of d, the pitch (ϕ) and azimuth (α) can be computed as

(see inset photo, above right for equations)

where σ_EIN is the North/East relative positioning uncertainty, σ_U is the vertical relative positioning accuracy, and d_h is the horizontal distance between the receivers. In other words, the longer the inter-receiver separation the better the attitude accuracy.

To give some numbers, for the lower- end measurement errors and DOP values listed at the start of this section, and nominal horizontal receiver separation of 1 meter, the pitch and azimuth accuracy would be 1.3 and 1.9 degrees, respectively. This is a one-off accuracy estimate and further filtering/ averaging would yield even better performance.

Summary
This article has given an overview of how GNSS can be used to determine the attitude of an object. Although reliant on ambiguity resolution, the short baselines involved make the process quite robust. The result can be highly accurate attitude estimates that can be applied to a wide range of applications.

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Enrico Salvatori, Qualcomm Europe

Carlo Bagnoli, STMicroelectronics

Multinational semiconductor and telecommunications company Qualcomm is a world leader in the design and marketing of 3G, 4G and next-generation wireless technologies. Headquartered in San Diego, California, Qualcomm has been widening its footprint in the Europe, Middle East and Africa (EMEA) region, with a core focus in Europe.

“We expect to grow Qualcomm’s presence in Europe, becoming a major EU (European Union) player in the digitization of European industries,” said Qualcomm senior vice president and president of Qualcomm Europe, Enrico Salvatori.

Multinational semiconductor and telecommunications company Qualcomm is a world leader in the design and marketing of 3G, 4G and next-generation wireless technologies. Headquartered in San Diego, California, Qualcomm has been widening its footprint in the Europe, Middle East and Africa (EMEA) region, with a core focus in Europe.

“We expect to grow Qualcomm’s presence in Europe, becoming a major EU (European Union) player in the digitization of European industries,” said Qualcomm senior vice president and president of Qualcomm Europe, Enrico Salvatori.

Part of that growth entails the company’s recently announced acquisition of Dutch-based NXP Semiconductors, a leading supplier for the secure identification, automotive and digital networking industries, a deal expected to be sealed by the end of calendar year 2017.

Key European Considerations
“Accurate, reliable, and rapid position location is an important part of the mobile experience,” Salvatori said. “We are currently continuing 5G standardization in 3GPP under an accelerated timeline, which will ultimately achieve results for all use cases for extreme mobile broad-band (MBB), massive Internet of Things (IoT) and mission-critical services, in line with the European Commission’s 5G Action Plan.”

The Commission, which is the executive arm of the EU, has been actively promoting very high-capacity networks such as 5G as Europe works to keep pace in the global wireless technologies market, citing expected worldwide 5G revenues for mobile operators in the region of €225 billion per year by 2025 (about 270 billion USD).

Salvatori said Qualcomm is proud to have established a long-lasting partnership with the European Commission (EC) and, as it happens, with the European GNSS Agency (GSA), sharing that agency’s central goal of bringing Europe’s global satellite navigation program, “Galileo,” to full fruition.

“Here at Qualcomm,” Salvatori said, “we are pursuing ongoing work on various IoT verticals, including LTE MTC/ NB-IOT and in particular C-V2X. And with LTE Release 14, we believe we can lead the way toward dedicated evolutions of 5G in addition to the NR.”

Salvatori is well-positioned to speak on such matters. He oversees Qualcomm’s European strategy for ensuring that OEMs and operators drive the latest G technology (4G/5G) adoption throughout all of Europe, in both developed and emerging markets.

Galileo in its Proper Place
For companies like Qualcomm looking to make the most of Europe’s emerging navigation and location-based services markets, the launch of live Galileo services last year was a veritable milestone.

“We were thrilled to see the European satellite system starting operations,” Salvatori said, “a real turning point for the location industry. We strongly believe that broad availability of Galileo will underpin Europe’s future innovation at home and globally, providing a backbone for the further development of the Digital Single Market and Europe’s industrial growth and competitiveness in the context of 5G, but also to the benefit of the new connected verticals.”

European authorities expect the addition of another GNSS, in the form of Galileo, will enable more accurate location performance, faster time-to-first-fix, and improved robustness all over the world, particularly in challenging urban environments where the combination of narrow streets and tall buildings can reduce accuracy.

And the launch of Galileo services was in many ways a victory for Qualcomm itself; as long-standing partners, the EC, the GSA and Qualcomm have worked in concert, with the GSA consistently highlighting Qualcomm’s role as a central player in the EU wireless technologies arena. The first European Galileo-enabled smartphone, produced by Spanish company BQ, was based on a Qualcomm chipset.

In fact, Salvatori said, “Qualcomm Technologies began implementing hardware support for Galileo several years ago in selected chipsets. And it was our company that proposed the mobile industry’s first pervasive, end-to-end, location services platform (Qualcomm® Location), for smartphone, computing, infotainment, telematics, and IoT applications.”

With optimized software enhancements, Qualcomm® Location now uses up to six satellite constellations. “Our users now benefit from more than 80 different satellites when calculating global position for navigation or location-based applications,” he said.

Salvatori said Qualcomm’s Galileo-enabled platform is being deployed broadly across the company’s modem and application processor portfolios. “This feature is integrated in the latest Qualcomm Snapdragon 800, 600, and 400 processors and modems,” he said. “And Galileo will be supported on smartphones and computing devices with the appropriate software release on Snapdragon 820, 652, 650, 625, 617, and 435 processors.”

The same goes for automotive infotainment solutions using Snapdragon 820A, and telematics and IoT solutions with Snapdragon X16, X12, X7, and X5 LTE modems, and Qualcomm 9×15 and MDM6x00 modems.

“This will also enable infotainment and telematics solution providers to satisfy an important component of the European eCall mandate ahead of the March 2018 deadline,” Salvatori said.

Low Investment, High Benefits
Salvatori said bringing Galileo into Qualcomm’s already broad location services platform presented no particular technical issues. “Supporting a new GNSS constellation does require some R&D,” he said, “but we had already implemented support for other GNSS constellations like GPS, GLONASS and BeiDou.

“For us, Galileo’s introduction in the market at commercial scale promises more innovation and business models for the expanded connectivity needs of tomorrow. This is particularly clear to us in areas such as automotive, where Galileo’s capabilities will underpin the evolution of cars toward ever greater connectivity and automation.”

Salvatori said Qualcomm’s support of Galileo is an essential extension of the company’s work in creating and evolving vehicle-to-everything communications and 5G, working closely with the mobile and automotive industries to bring these innovations to market fast and with sustained investments.

THINGS YOU SHOULD KNOW
3GPP—Third Generation Partnership Project, a collaboration between groups of telecommunications associations, known as the Organizational Partners.
LTE—Long Term Evolution, applying to the idea of improving wireless broadband speeds to meet increasing demand.
MTC—Machine Type Communication, also known as machine-to-machine communication, i.e. direct communication between devices.
NB-IOT—Narrow-Band Internet of Things, a Low-Power Wide-Area Network radio technology standard enabling a range of devices and services to be connected using cellular telecommunications bands.
C-V2X—Cellular Vehicle to Everything, combining features of V2V (Vehicle to Vehicle), V2I (Vehicle to Infrastructure), V2P (Vehicle to Pedestrian) and V2N (Vehicle to Network).
LTE Release 14—3GPP standards are structured as Releases. Discussion of 3GPP thus frequently refers to the functionality in one release or another.
NR—5G New Radio, aimed at bringing fiber-like performance to wireless broadband at a significantly lower cost per bit.
UMTS—Universal Mobile Telecommunications System, a third-generation mobile cellular system for networks based on the GSM standard.
HSPA—High Speed Packet Access commonly refers to UMTS-based 3G networks that support specialized data for improved download and upload speeds.

R&D FOREFRONT
QUALCOMM IS CURRENTLY LEADING THE “CONVEX” CONSORTIUM, which also includes Audi, Ericsson, Swarco Traffic Systems and the University of Kaiserslautern. The group’s aim is to set up a test bed for first LTE Rel. 14 trials for V2X and to validate performance and feasibility.

The project combines techniques for vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-person, (V2P), and vehicle-to-network (V2N) communications, which are considered key components for the implementation of advanced driving assistance systems and for automated driving.

The project is funded by the German Ministry of Transport and Digital Infrastructure (BMVI) in the program “Automated and Connected Driving on Digital Test Fields in Germany.”

The post GSA’s GNSS Opinion Leaders for September 2017 appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Enrico Salvatori, Qualcomm Europe

Carlo Bagnoli, STMicroelectronics

Carlo Bagnoli is Director of Infotainment BU System and Applications at STMicroelectronics. The company is a global semiconductor leader focusing on smart driving and the internet of things, creating intelligent and energy-efficient products that enable intelligent transport as well as smarter factories, cities and homes.

Within the infotainment business unit, Bagnoli and his team work to develop positioning receivers, broadcast receivers and communication processors for the automotive market. Doing so means gathering GNSS signals from far and wide.

Carlo Bagnoli is Director of Infotainment BU System and Applications at STMicroelectronics. The company is a global semiconductor leader focusing on smart driving and the internet of things, creating intelligent and energy-efficient products that enable intelligent transport as well as smarter factories, cities and homes.

Within the infotainment business unit, Bagnoli and his team work to develop positioning receivers, broadcast receivers and communication processors for the automotive market. Doing so means gathering GNSS signals from far and wide.

Once upon a time, Bagnoli says, “everybody thought GPS was enough. Now it’s the multi-constellation system that is a sort of de facto requirement.”

Coming Up
Early believers in the power of multiconstellation in situations such as urban canyons STMicroelectronics beat all major competitors to the punch when it unveiled its dual-constellation, GPS+Glonass receiver in 2011. But in fact the company had already been working for years on blending GPS+Galileo signals.

“We started in 2004 with some high-level exploratory work,” Bagnoli explained, “ and then we did our first funded research under the European Union’s FP7 Program, working to develop a Galileo-ready positioning terminal. Based on the outcome of that research we created the navigation CPU CartesioPlus product.”

So Galileo was already present in ST’s GPS/GNSS receiver hardware by the mid-2000s, with a new RF and an FPGA-based baseband. A production version of CartesioPlus followed in high volume from 2009, but it was in reality still a GPS-only chipset, because there were not yet any operational Galileo satellites in orbit.

“We were still looking into increasing the number of supporting constellations,” Bagnoli said, “So with Galileo still under development, we began working on a new product that could also support Glonass, called ‘Teseo’, which we completed and launched in 2011.”

By then, he said, the rest of the mobile industry had already understood that Glonass was quite relevant for improving the user experience in urban canyons. However, he said, the rest of the automotive mission-critical industry was late: “With Teseo, putting together the first multi-constellation chip, we anticipated the work of the others by 12-24 months.”

Today, Bagnoli says, the accuracy of Glonass has improved, through better geometry, but not because of the accuracy of the actual signal, which is still a problem.

Galileo is Born
For STMicroelectronics, the launch of Galileo initial services in December 2016 was a real breakthrough, Bagnoli said: “For a GNSS receiver company, the birth of any new Open Service GNSS system has to be considered an opportunity for new integration as it improves the user experience with no major steady-state cost added.”

The case for Galileo, he said, was a no-brainer. “ST decided to include Galileo from its inception and we have had it both in our CartesioPlus navigation CPUs and in our dedicated standalone Teseo receivers. And we are completely committed to including it on our next-generation multi-band precise positioning platforms.

“With Galileo there is no major cost of development; the development applies to multiple system-on-chip platforms, so the relative effort of adding Galileo to a set of already -supported constellations is very manageable.”

Bagnoli said the his company definitely made the right decision in preparing for Galileo early: “ST is focused on automotive and ITS and in these markets research and development cycles take significant time. Thanks to foresight, we now have two mature product families and we are looking forward to the business development of our Galileo-capable receivers.”

Multi, Multi, Multi…
Everyone seems to agree; a new type of mission-critical GNSS receiver is now needed, one that can work in conjunction with correction data available from multiple sources, ultimately providing sub-meter accuracy.

To this end, Bagnoli said, STMicroelectronics is looking at ways to combine all the functional GNSS constellations: “Finally, as we have more and more automotive ITS, integrity is becoming more and more important, so redundancy is useful and, for example, cooperative multi-constellation anti-spoofing is something that we have already worked on.

“We now have a solution where we do very fine monitoring of the different systems in order to provide more integrity beyond the accuracy dimension, and this is going to be particularly relevant for regulated services and so on.”

Indeed, while these single- frequency, multi-constellation GNSS solutions are still viable for traditional Infotainment applications, emerging intelligent transportation systems (ITS) and liability- and safety-critical applications such as advanced driver assistance systems (ADAS) are raising performance and integrity requirements for GNSS receivers, creating demand for new and even more advanced GNSS solutions.

Bagnoli describes the clear advantages of a rapidly maturing Galileo system in terms of its high accuracy and high integrity, the latter of which he says has been an overlooked aspect in many markets. “If your aim is to increase accuracy, augmenting GPS through a multi-constellation configuration, then Galileo is really the best way to go, particularly for automotive and ITS systems; And its integrity features will serve well in support of regulated services.

“Right now, with Beidou3 still under construction, GPS+Galileo is going to be a very solid mode, and this is the only harmonized pure L1 receiver combination available.”

Again, Bagnoli says, for his company the key market for the Galileo open service is ITS, including liability- and safety-critical applications. “STMicroelectronics is focusing to this market and aiming to be a key player there,” he said. “Further, we expect that this will become a multi-frequency as well as a multi-constellation equation, as more augmented driving applications arrive, and we are excited by the challenge and the business opportunity.”

The post GSA’s GNSS Opinion Leaders for August 2017 appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Collision avoidance will be more practically and universally achievable, even in skies crowded with unmanned aerial vehicles (UAVs), if the aviation community takes advantage of the raw measurements already present in today’s GPS receivers, but largely ignored in favor of using GPS position coordinates (see “The Advantages of Raw Measurements” sidebar, below.) Changes to existing aviation equipment could enable aircraft to estimate the flight paths of other aircraft far more accurately enabling safer operations even under impaired conditions. The use of the raw data, once integrated into standardized flight protocols, could dramatically help prevent midair accidents even if one or both of the aircraft is unmanned.

This flight-validated approach uses established algorithms, readily available universal access transceivers (UATs) and existing communication message formats (as described by P. Duan et alia in Additional Resources). There is one essential departure from current practice: including the raw pseudorange and carrier phase data within the automatic dependent surveillance-broadcast (ADS-B) messages in place of the derived position coordinates. This same approach could be applied with great benefit should a separate system, similar to ADS-B, need to be developed to support UAV operations.

For more than 50 years it has been feasible to combine intermittent partial data – of different types at varying accuracies with different sensitivities from different directions at different times – and extract all benefit therein. The seemingly unspectacular step of using raw measurements in the message opens the door to using powerful, widely understood methods for predicting flight paths — and therefore the points of potential collision — and handling situations where position determination is hampered due to jamming or because there are not enough satellites in view. It should be possible to do all of this for aircraft at various altitudes without building and certifying extensive new radar or other ground infrastructure.

This paper primarily involves GPS and airborne operation, but a half-century of experience combining data enables this technique to be dramatically extended. Integration of different sensors (eLoran, DME, etc.) is straightforward; a claim that has been verified and documented.

Raw Measurements Improve Estimates
For a host of reasons, techniques using raw measurements — which are present in any navigation sensor — will outperform by orders of magnitude techniques relying on position reporting. Differential GPS (DGPS) owes its spectacular success to its use of raw measurements. A Kalman tracker uses weights based on an extensive array of data. There are across-axis correlations between error components in different directions, between components of position and velocity, etc. — and the sensitivity of each individual observation to every one of those components is taken into account.

Unfortunately none of those features can be used when starting with coordinates derived from raw measurements. Since ADS-B link bandwidth can’t hold its existing content plus all that correlation information, ADS-B messages contain no correlations — but airborne computation armed with a history of raw measurements can deduce all. The contrast could hardly be more compelling.

The extended squitter message can remain unchanged except for the replacement of position and velocity by raw measurements. Since navigation systems commonly allow multiple message types, however, position reports are not strictly ruled out. Occasionally another message type could be used to broadcast the position for, say, track file initiation. To realize the performance potential, however, messages containing raw measurement data would be far more frequent.

But why go to the trouble of changing an established methodology (that is position reporting) for one based on Kalman filtering considerations? There are multiple reasons. Probably most obvious, instantaneous position is fleeting for anything airborne; data must be combined. Even satellite navigation uses several observations to get a position fix (i.e., four satellites in air). Another reason stems from the nature of the position that must be determined: To support collision avoidance the position information for one object must be determined relative to the other objects in nearby space and projected into the future. Using raw data makes these calculations far, far more accurate.

Consider, for example, a pair of position coordinates, one with perfect longitude but a kilometer of error in the north direction, and the other with exact latitude but its east/west position is off by a kilometer. Averaging them gives “only” 500 meters of error in both!

Even if different tolerances of different position reports are taken into account, ignoring wide variations in sensitivity and correlation parameters is ruinous. There are no tight velocity accuracy requirements specified for ADS-B as a result.

Accurate Velocity Essential
In fact the velocity requirement for ADS-B is loose in multiple ways. For characteristics that matter in regard to collision avoidance, velocity is a vector — a vector relative to other objects in nearby space regardless of those objects’ latitude or longitude — and with errors having statistical properties. Error values of several meters/second, even as much as 10 meters/second, have been published in connection with ADS-B. A more subtle point is that even a substantially lower 1 meter/second error value is dangerous statistically. Without detailed elaboration, this much needs to be recognized: extreme value theory (EVT) shows that, even if all errors were Gaussian, mixed Gaussian probability offers far less assurance than intuition would suggest. Instances of exceeding 10 sigma cannot be discounted (see Farrell J., and F. van Graas, Additional Resources). To ignore that is to accept an excessive and unsafe risk; “unlikely” is often not unlikely enough. A 10 meter/second error is therefore not too farfetched to consider. A sequence of position reports can suffice for transoceanic flight but not within crowded airspace.

Avoiding mid-air crashes requires a fresh look at system priorities. For example it is not critical to have highly accurate position reports. For airliners with wingspans tens of meters long moving at hundreds of kilometers per hour, precise position is fleeting and unnecessary. A few meters of current position error will be insignificant and working to refine that position error would be pointless.

It is essential, however, to have highly accurate velocities. The product of (velocity error) × (time to closest approach) is dominant when it comes to collision avoidance. Instead of meters per second velocity accuracy, airliners need centimeters/second accuracy; otherwise the projected position over time is so crude as to be useless for collision avoidance.

Collision avoidance demands assurance of sufficient distance at time of closest approach. That clearly requires accurate knowledge of velocity — specifically the relative velocity vector. Stitching coordinates together from reports of latitude + longitude + altitude (“LLH”) cannot deliver that, which explains why ADS-B does not promise good velocity. Errors of 10 meters/second have been published with little elaboration (not relative, not vectorial, and with no statistical boundaries). If the closest approach is a minute away, then even the most elementary arithmetic assigns 600 meters of uncertainty to that future position.

Consider, for example, two aircraft flying at the same altitude: “Ownship” at location O with velocity VO and “Another-ship” at location A with velocity VA (see inset photo, above right). They are instantaneously separated by vector R which, for closing scenarios, is shrinking. The closest approach will occur at time T when the component of relative velocity (vector difference VA – VO, not shown) parallel to R passes through zero — the perpendicular component is miss distance. That simple scenario has appeared in countless context-dependent forms (e.g., with intruder at A and evader at O in or with target at A and an interceptor or projectile at O in military operations). Determination of T and minimum separation distance follows easily from relations just stated, readily superseded whenever maneuvers subsequently change either velocity.

Now to confirm the case for precise velocity: rather than current position, collision avoidance requires accurate future position (i.e., at time T). When position is projected T seconds ahead, a 10 meter/second velocity error will cause that predicted future position to be in error by 10 T meters. With that much error in each of two horizontal axes that product will be squared, producing an unacceptably large area of uncertainty. Trying to steer away from an unknown place has no meaning. The Traffic Collision Avoidance System (TCAS) uses climb/dive maneuvers instead. Imagine that becoming a commonplace event as the skies fill with unmanned aircraft.

There are many facets to this subject, but the simple, fixed-altitude case above is useful for establishing some fundamentals:

• Time T used above matches the “tau” of the traffic collision avoidance system (TCAS) only on a collision course
• Effects of current position error matter far less than velocity error 
• Centimeter/second accuracy for velocity rather than meters/ second can enable horizontal evasive strategies 
• Longer values of T (earlier evasive action) is also thereby made possible 
• Earlier evasive action is highly preferable to TCAS’s abrupt violent maneuvering 
• TCAS cannot act early because valid decisions require accurate tracks 
• TCAS tracks are informed by accurate range but very crude cross-range data 
• TCAS cross-range information improves only as the sightline rotates 
• Sightline rotation increases at close range — exactly the waterloo for collision avoidance! 
• The precise satellite navigation data used in the previously mentioned P. Daun et alia article (Additional Resources) provides full 3-D tracks quickly 
• Requisite speed changes and T have been quantified for many cases (see Farrell, J., “Collision avoidance by speed change,” Additional Resources).

Raw Measurements Improve Tracking
As noted in the article on airport surface surveillance (J. Farrell and E. McConkey, Additional Resources) computers now can easily maintain integrated track files for every participant in any scenario. Even in the 1970s two missiles plus two aircraft were simultaneously tracked in real time with an electronically steered radar antenna at White Sands. The estimation algorithms in the White Sands case were fed by raw observations (range, azimuth and elevation in that case) — never with coordinate pseudo-measurements — and tracking from high dynamic platforms with “Ownship” navigation is a straightforward extension of tracking from a stationary location.

Today’s computing capabilities readily enable each participant to maintain a bank of extended Kalman filters (EKFs) with a separate track file for each participant and with every participant having a designated slot in the sequence of transmitted messages from all the participants. The full set of participants should include every object that could be involved in any collision. The track file in any participant’s database is not tied to coordinates; it’s scalar. From those scalars each participant can construct a set of vectors and all those vectors will be correct and can be expressed in his own perceived reference. If that perception differs from the other participants’ (due to misalignments or even a different datum), performance does not suffer one iota.

Air-to-air tracking has always placed the Ownship described in the example above at the center of its “own little world” without any degradation. What matters is relative state (position, velocity…) in Ownship’s “own little world” expressed and maintained consistently the same way for all. Sharing satellite navigation data with others will not introduce any error since those measurements are scalar —unattached to any coordinate frame. If the presence of one participant with overriding authority must be identified, one of the participants could be a tower. With the exception of the tower, if there is one, moving participants would make path adjustments with each message received as the scenario unfolds. Those smaller, repeated adjustments over time will prove far less abrupt than making a start-from-scratch change at close range.

Dealing With Too Few Satellites
Using the raw data also enables the development of track files in situations where there are not enough GPS satellites in view. In fact, in some urban canyon scenarios it is possible to have situations where there are never enough satellites available with good enough geometries.

As things now stand, if an aircraft’s GNSS receiver does not have enough satellites in view it is not able to determine its position and therefore has nothing to broadcast on ADS-B. That is a scandalous waste of very accurate information. Raw data measured every second or so will give you a far better track file than the usage of GPS coordinates. Stitching coordinates together to get velocity gives totally inadequate performance. That is why ADS-B, even with all the ADS-B Out and ADS-B In information, will not provide accurate velocity.

UAV-Specific Considerations
While UAVs will be responsible for taking evasive action, they will be less burdened in other respects. Their lower speed affords multiple advantages: more time for evasion, track file initiation at short range (allowing operation at low power) and the ability to make tighter turns. All of these factors make sense and avoid easier for UAVs than it is for fast-moving airliners.

Nor will UAVs require the sophistication used by P. Duan et alia in Additional Resources, which used 1-second changes in meticulously prepared carrier phase measurements. Many satellite navigation receivers don’t use carrier phase but all have pseudoranges — those will suffice as long as they are made available with appropriate time stamps. Also, decimeters/second rather than centimeter/second velocity error will be acceptable — again because of a UAV’s slower speed. With evasion by acceleration or deceleration, for example, the simple program (see J. Farrell, “Collision avoidance by speed change,” in Additional Resources) can just have different parameters. Finally, evasion strategy won’t be limited to speed changes; descent or turns can be used in some circumstances.

The Challenge
Though the advantages of using raw measurement are clear, change is not easy. Using position, and over recent decades GPS-derived position, in ADS-B messaging has long been the established approach. However the integration of unmanned aircraft is such a monumental challenge that new techniques and air traffic management systems for UAVs are being considered. Incorporating raw measurements not only offers a capability that supports safe UAV integration, but offers real advantages to manned flight operations as well — and there is a rock-solid track record supporting both double differencing (see “Double Differencing” sidebar, below) and all modes of tracking (air-to-air, air-to-surface, surface-to-air, surface-to-surface) by adaptive modern estimation.

Conclusions and Recommendations
Working with the raw measurements instead of relying only on the position calculated using those measurements makes it possible to apply the techniques that made differential GPS so spectacularly successful. This approach also opens the door for the integration of data from information sources completely different from GNSS and from each other. Raw measurements offer the only way to achieve true integration with systems like DME, eLoran and Iridium and, especially for cooperating UAVs, signals-of-opportunity (see R. Kapoor et alia, Additional Resources).The scope can also be extended to include observations of nonparticipants (see Fig. 9.4 in J. Farrell, “ GNSS Aided Navigation and Tracking – Inertially Augmented or Autonomous,” in Additional Resources).

The improvements in situational awareness are dramatic enough to suggest redefining availability and continuity of operation. Less obvious but equally decisive is how this approach strengthens integrity. Every individual measurement can be acceptance-tested — directly, easily, and independently of all others, supported by demonstrated equivalence to rigorous, widely accepted parity methods (see “Integrity Testing: Ultra-simple And Rigorously Validated” sidebar, below).

These dramatic improvements do not require new discoveries or the invention of new equipment. A revision of the ADS-B message content, hopefully via a software update — and the inclusion of raw measurements in any new system developed to support UAVs — will enable a host of spectacular benefits from already readily available data. In fact the use of raw measurements is so promising that SAE International has begun developing standards to bring this approach into the mainstream.

There also are documented non-proprietary navigation algorithms already available that make it possible to tap the value of the raw measurements. These algorithms could help keep costs down and speed the launch of a pilot project to test this approach, especially in the case of unmanned aircraft. There is enormous commercial, political and regulatory pressure to integrate UAVs into the national airspace. A pilot project could support both manned and unmanned aviation by strengthening reliability and robustness while boosting accuracy and integrity — thereby helping keep aircraft out of each other’s way.

An old movie scene showed Bob Hope trudging through a desert, desperately uttering “water, water” — then finding himself waist deep in a stream moments later, mumbling “mirage, mirage.” The advantages of using raw measurements for ADS-B and systems similar to ADS-B are not a mirage. Between what we know and what we do is a wide gulf. Let’s close it.

Appendix—Additional Topics
Two separate but related articles from a recent Institute of Navigation newsletter discuss important developments in GPS/GNSS interfacing. Starting on page 1 and continued on page 7, the first describes major improvements in Android handsets. The second, on pages 14-15, announces formation of a Society of Automotive Engineers (SAE) International working group, which will work on the standards cited in the Conclusions and Recommendations section of this article, ensuring the extension of benefits to the vast majority of devices. (SAE International is a global association of more than 128,000 engineers and related technical experts in the aerospace, automotive and commercial- vehicle industries.) These were preceded by other publications emphasizing the benefits offered by working with measurement data. One, more than 25 years old (J. Farrell and F. van Graas, Additional Resources) was in fact preceded by an obscure (1977) NAECON paper. Two more recent videos here and here plus a presentation offer additional background.

The centimeter/second residuals achieved in flight test described previously by P. Daun et alia, in Additional Resources, were obtained by using sequential changes in carrier phase measurements measured once a second. Unlike the carrier phases themselves, 1-second changes in them are interoperable (i.e., regardless of different timing and/or geoid conventions used for separate constellations) and immune to catastrophic error (see links to https://jameslfarrell.com content in Additional Resources). Furthermore, because two main sources of propagation error change very little over a second, there is no need for a mask angle — a trait that benefits geometric dilution of precision (GDOP) for velocity.

Additional Resources
1. Bayliss, E., R. E. Boisvert, M. L. Burrows, and W. H. Harman, “Aircraft surveillance based on GPS position broadcasts from Mode-S beacon transponders,” ION-GPS94.
2. Duan, P., M.U. De Haag and J. Farrell, “Flight test results of a measurement-based ADS-B system for separation assurance,”, NAVIGATION, Journal of the Institute of Navigation, Volume 60, Number 3, 2013, pp. 221-234; Abstract
3. Farrell, J. and E. D. McConkey “Quantum improvement in airport surface surveillance,” IONNTM, 1998
4. Farrell, J., E. D. McConkey, and C. G. Stephens, "Send measurements, not coordinates," NAVIGATION, Journal of the Institute of Navigation, Volume 60, Number 3, 1999, pp.203-215). 
5. Farrell, J., GNSS Aided Navigation and Tracking — Inertially Augmented or Autonomus, American Literary Press, 2007
6. Farrell, J., "Collision avoidance by speed change," COORDINATES Volume VIII Number 9, Sept. 2012, pp. 8-12
7. Farrell, J., “Letters: Get a Start on GNSS Interoperability Now,”
8. Farrell J. and F. van Graas, “That all-important interface,” James L. Farrell and Frank van Graas, ION-GPS90
9. Farrell J., and M. L. Farrell, “ADSB (2nd-) Best Foot Forward?” Journal of Air Traffic Control, Summer 2008, 44 17-18.
10. Farrell J. and F. van Graas, “Containment Limits for Free-Inertial Coast,” ION-GNSS2010
11. Farrell, J., "Single-Measurement RAIM"
12. Farrell, J., "Send Measurements, Not Coordinates" — pages 14-15
13. Farrell, J., "Dead Reckoning by GPS Carrier Phase"
14. Farrell, J., "1-sec Carrier Phase (again)"
15. Kapoor, R. S. Ramasamy, A. Gardi, R. Sabatini, “UAV Navigation Using Signals of Opportunity in Urban Environments: An Overview of Existing Methods,” 1st International Conference on Energy and Power, ICEP2016, 14-16 December 2016 (Available online here)
16. SAE International, Remote Identification and Interrogation of Unmanned Systems
17. Van Sickle, G., “GPS for military surveillance,” GPS World, Nov. 1996. 

SIDEBAR: The Advantages of Raw Measurements

A 2012 flight validation used GPS without augmenting system corrections but with raw measurements from receivers. Twenty years earlier Lincoln Labs successfully demonstrated GPS broadcasts with Mode S beacon transponders at Logan Airport as described in E. T. Bayliss et alia. (Note: the 2012 flight in the first case used UATs instead of Mode S). Transmitted positions enabled each participant to track every other participant’s data while minimizing or eliminating garble, by replacing conventional interrogations with information in assigned time slots (as ADS-B currently prescribes).

One basic modification of the Lincoln Labs methodology was advocated in the work by J. Farrell and E. McConkey and linked with another system — the Joint Tactical Information Distribution System (JTIDS) to form a more general application. Instead of coordinates, the transmitted message’s 48 information bits can contain raw uncorrected measurements. Data compression and the cycling of in-view GPS satellites can mitigate bandwidth limitations.

The introductory paragraphs of an earlier article titled “Send measurements, not coordinates,” (J. Farrell et alia) noted eight crucial advantages. In combination with each — tracking-every-other feature already noted — a later expansion of that paper (J. Farrell and M. Farrell, Additional Resources) offered an even more extensive list of advantages:

• Two decades of stunningly successful differential GPS operations demonstrate this approach
• Error source cancellation capability is intrinsic to differential GPS 
• The ability to account for specific sensitivities of each individual measurement 
• The opportunity to employ those sensitivities to assign data weighting adaptively 
• Widely known techniques for minimization of statistical error resulting from that adaptivity 
• Prompt determination of full information (cross-range as well as along range) 
• Presence in that information of accurate dynamics as well as current position 
• Ability to use the dynamics to anticipate time of closest approach 
• Ability to deduce, from the dynamics, the miss distance at that future time 
• Ability to resolve conflicts by turns or speed change instead of climb/dive 
• Applicability to both 3-D (in-air) and 2-D (runway incursion) encounters 
• Removal of potential danger in the event of datum reference non-uniformity 
• Full usage of available data when too few satellites are visible for a full fix 
• Integrity checks enabled with any number of satellites observed 
• Unrestricted algorithm release (no strings attached or proprietary claims) 
• No need for augmentation (corrections) from ground stations 
• Opportunity for participants to share observations of nonparticipants 
• Retention of applicability with or without prospective modernizations 
• Insensitivity to different models used in different constellations

These benefits are utterly absent if calculations must rely only on instantaneous position reports.

SIDEBAR: Integrity Testing: Ultra-simple and Rigorously Validated

Volumes have been written on Receiver Autonomous Integrity Monitoring (RAIM), often supported by sophisticated analytical methods and substantial mathematical development. The good news is the hard work has been done. All a program needs is a set of expressions to put into code. Even more fortuitous is further simplification of those expressions—and that also has been done. Moreover, the way that simplification has been done allows extension beyond GNSS, to include every morsel of data used for navigation.

Conventional RAIM uses five satellites for fault detection and six satellites for fault exclusion or isolation. Because every subset of four within those sets must support adequate geometric dilution of precision (GDOP), exclusion or isolation is not always available. Then, when the five-satellite detection indicates excessive error, conventional RAIM rejects the whole quintet, the good along with the bad. Forcing valid data to suffer from “guilt-by-association” is extremely wasteful and unnecessary. See here. Reversing the loss is especially urgent when data availability is marginal. A variety of advanced integrity features offers:

• Addition of cyclic bias estimation without changing navigation solutions
• Circumvention of parity vector operations added for conventional fault isolation/ exclusion 
• Replacement of that parity vector by a parity scalar with no loss of capability 
• Normalization of that parity scalar to a form with variance equal to one (dimensionless) 
• Accounting for effects of correlations incurred by differencing 
• Inclusion of closed form matrix solutions for fault detection and isolation/ exclusion with correlations 
• Extension to separate validation of each individual measurement, whether others are present or not 
• Opportunity to verify single-measurement tests when multi-satellite isolation/ exclusion is feasible 
• Support by rigorous theory (matrix decomposition etc.) with no need to employ it in operation. 
• The normalized parity scalar test for every individual measurement (everyone understands a dimensionless scalar random variable with sigma = 1) provides a vital means of operating with any and every available source of navigation information.

SIDEBAR: Double Differencing

Ignoring wide variations in sensitivity and correlation parameters is ruinous, but it is possible to recover that information.

In August 2000 I presented the raw measurements-in-squitter-messages concept as a natural extension of GPS double differencing and asked RTCA SC186WG4 members to imagine two happenings:

• Let every system and every plan in existence be only supplemental/ backup
• Let every participant compare his own data from each separate satellite to corresponding measurements from all other participants, weighting every individual difference adaptively according to its information content (we’ve been optimizing partial information weights for a half century).

A rock solid track record supports double differencing and all modes of tracking (air-to-air, air-to- surface, surface-to-air, surface-to-surface) by modern estimation. A sequence of position reports can suffice for transoceanic flight but not within crowded airspace. As noted in [4] computerized “bookkeeping” can easily maintain track files for every participant in any scenario.    

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1996 soccer game in the Midwest, (Rick Dikeman image)

Nouméa ground station after the flood

A pencil and a coffee cup show the size of NASA’s teeny tiny PhoneSat

Bonus Hotspot: Naro Tartaruga AUV

Pacific lamprey spawning (photo by Jeremy Monroe, Fresh Waters Illustrated)

“Return of the Bucentaurn to the Molo on Ascension Day”, by (Giovanni Antonio Canal) Canaletto

The U.S. Naval Observatory Alternate Master Clock at 2nd Space Operations Squadron, Schriever AFB in Colorado. This photo was taken in January, 2006 during the addition of a leap second. The USNO master clocks control GPS timing. They are accurate to within one second every 20 million years (Satellites are so picky! Humans, on the other hand, just want to know if we’re too late for lunch) USAF photo by A1C Jason Ridder.

Detail of Compass/ BeiDou2 system diagram

Hotspot 6: Beluga A300 600ST

1. ANTARCTIC OASIS
Antarctic Peninsula

1. ANTARCTIC OASIS
Antarctic Peninsula
√ The “Antarctic oasis” is how polar researchers refer to the north end of James Ross Island, which at the extreme northern tip of the Antarctic Peninsula, is shielded from storms by the Trinity Mountains. For researchers at the Johan Gregor Mendel Research Station located on the Ulu Peninsula at the far north end of the island, the time and resources available for accurate positioning are limited, therefore, the processes to capture positions must be simple, efficient and combine readily with the scientific activities. One of the most important uses of GNSS at the Mendel Polar Station is for monitoring glaciers. Scientists are studying four glaciers on the island within 15 kilometers of the station. On three glaciers, networks of bamboo rods are installed into the ice at regular intervals. Researchers use GNSS to measure the 3D position at the base of each rod. They also measured the distance from the top of the bamboo to the ice surface. The data collected will support months and years of processing and analysis. Plans are already underway for future visits by Czech teams to James Ross Island.

2.  WHERE’S THE BEEF?
Wageningen, The Netherlands
√ This E-Track project uses the European Geostationary Navigation Overlay Service (EGNOS) to provide higher accuracy locations than in conventional GPS animal tracking. It developed GPS animal tracking and analysis tools for sophisticated behavioral research on wild and domestic animals and focuses on enhanced accuracy, combined with fast sampling, sensors and a wide range of tag formats, sizes and remote communication systems.
The system is validated in field studies with mammals and large birds and uses devices enhanced by using the EGNOS augmentation system. The different GPS tags, either implemented as backpacks (birds) and collars (mammals) collect raw GPS signals to either calculate high precision “on board” or in post processing mode. GPS + EGNOS provides submeter accuracy. The devices developed in E-Track also include 3D accelerometers, necessary for the distinguishing behaviors with almost similar spatial patterns. It features a tracking software solution that is marketed via Noldus Information Technology, and the E-Track is carried out in the context of the Galileo FP7 R&D program supervised by the European GNSS Agency.

3. FINDING THE POWER
Reykjavík, Iceland
√ New research, with lead authors from the University of Gothenburg, gives indications of the best places in Iceland to build thermal power stations. In Iceland, heat is extracted for use in power plants directly from the ground in volcanic areas. Constructing a geothermal power station near a volcano can be beneficial, since Earth’s mantle is located relatively close to the crust in those areas, making the heat easily accessible. But placing a power plant near an active volcano is not without risk, as an eruption can easily destroy any human-made construction in its way.

The scientists have now studied three different parts of the divergent ridge (area where the ocean plates are slowly sliding away from each other) that crosses Iceland from southwest to northeast. The slow movement and separation of the ocean plates can cause cracks in Earth’s crust, through which hot magma from the planet’s interior rises to the surface. As a result, many volcanos have emerged along the divergent boundary. Using a geodetic GPS, the scientists have now been able to measure the movement of the plates over time. The data used in the study is based on measurements from almost 100 “fixed” measurement points. The information from the measurement points have made it possible to draw maps that show in what way the plates are moving away from each other and how large the deformation zone is.

4. AUSTRALIA ON THE MOVE?
Symonston, Australia
√ Australia is indeed on the move, with the Pacific tectonic plate moving in a northeasterly direction by about seven centimeters each year. As of January 2017, Australia’s coordinates have officially moved 1.8 meters northeast, following the launch of the Geocentric Datum of Australia 2020 (GDA2020). The first update to Australia’s coordinate system in two decades, GDA2020 is a step towards modernizing Australia’s spatial referencing system.

The work of Australia’s experts from Geoscience Australia and the Intergovernmental Panel on Surveying and Mapping (ICSM) are behind the move to GDA2020; Australia is one of the first countries in the world to make the ambitious move towards a dynamic datum. It will have the valuable role of supporting future positioning needs for applications like driverless vehicles and centimeter-accurate personal navigation. Australia’s current datum GDA94, like most other national datums the world over, adopts an epoch to define the datum and is expected to differ over time from the ITRF datum used by satellite navigation systems like GPS.

The post GNSS Hotspots | April 2017 appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

The GPS community and Virginia-based Ligado are weighing new and upcoming test results as the standoff over interference with satellite navigation services enters its seventh year.

The dispute centers on the company’s now modified proposal to build a terrestrial wireless network supported by frequencies originally allocated for satellites. Though there had been a move some years earlier to augment the satellite services with ground stations the company’s first plan envisioned some 30,000 high-powered ground terminals.

The GPS community and Virginia-based Ligado are weighing new and upcoming test results as the standoff over interference with satellite navigation services enters its seventh year.

The dispute centers on the company’s now modified proposal to build a terrestrial wireless network supported by frequencies originally allocated for satellites. Though there had been a move some years earlier to augment the satellite services with ground stations the company’s first plan envisioned some 30,000 high-powered ground terminals.

Ligado’s frequencies neighbor the band relied upon by GPS and tests in 2011 showed the proposed network would overload the vast majority of GPS receivers. The Federal Communications Commission (FCC) put the project on hold early in 2012 and the company filed bankruptcy not long thereafter. It reorganized, emerging from Chapter 11 late in 2015 and soon changed its name from LightSquared to Ligado. It modified its network plan, but deep concerns over GPS interference continued.

Since exiting bankruptcy Ligado has been pressing the FCC for approval to build its network. Last year it poured more than $1.5 million into lobbying, according to filings with the Lobbying Disclosure Act database, and it funded studies of its own aimed at convincing the FCC to pull the broadband plan off the back burner.

The Results Are In
Ligado hired two technical organizations — Roberson and Associates, a telecommunications engineering firm, and the federal government’s National Advanced Spectrum and Communications Test Network (NASCTN).

Roberson released a study in May 2016 that, despite efforts to put the outcome in the best light, showed that Ligado’s broadband signals would impact several different GPS receivers. In December 2016, a summary of another Roberson test on signal reacquisition times, posted on the FCC docket by Ligado without comment, also showed impacts to some receivers.

Ligado used Roberson’s research to press for a change in the way interference is assessed. They had the firm measure position error as well as the carrier-to-noise-density ratio (C/N₀). Changes in the signal-to-noise ratio are the internationally accepted metric for determining levels of interference. Ligado asserted that position error was a better choice — a proposal that has, so far, failed to gain traction.

“Roberson’s use of measures of harmful interference other than the established standard of a 1 dB increase in the noise floor is not appropriate, as the record before the FCC amply demonstrates,” said Jim Kirkland, general counsel and vice-president of Trimble in a statement issued in response to the May 2016 Roberson test results. “Precision GNSS products like Trimble’s control important processes such as construction machine guidance at a level of precision that cannot be reliably observed by Roberson’s tests.” See here.

Using position error would favor Ligado’s signals and allow damaging levels of interference to GPS receivers, said Logan Scott, a GPS signal expert and a consultant specializing in radio frequency signal processing and waveform design for communications, navigation, and other systems. GPS receivers can give accurate position information even when experiencing serious interference, he explained in an interview last year. As long as they are tracking satellites they can provide fairly accurate positioning information right up to the point where the interference causes them to lose their lock on the satellite.

Though Ligado continues to press for a position error standard at least one expert thinks the matter is settled.

Tim Farrar, a technology consultant specializing in the satellite industry who has followed the Ligado saga closely, believes the firm has failed to persuade decision makers that position error is the better metric.

“They’ve lost that argument,” Farrar told Inside GNSS.

NASCTN Reports
Ligado’s project with the NASCTN also incorporated position error. NASCTN is a national network of federal, academic, and commercial test facilities formed by the National Institute of Standards and Technology (NIST), the Department of Defense (DoD) and the National Telecommunications and Information Administration (NTIA).

NASCTN’s self-described mission to accelerate deployment of wireless technologies raised doubts about its impartiality when the tests were announced — doubts that sharpened when it became clear Ligado, and not NASCTN, was crafting the research questions. Among the issues the project was designed to examine was the “preference” for using changes in C/N₀ over other measures, including position error, to evaluate interference.

The work got off to a controversial start with a test plan that was developed with limited input and then sprung on the GPS community — which then had just a few days over a weekend to provide feedback. NASCTN said at the time, however, that “to preserve its neutrality,” it would provide robust test methodologies and validated measurement data but “not make policy recommendations based on this data.” Moreover, NASCTN told Inside GNSS that it would “not make any policy-related interpretations associated with the test data,” although it “will likely make recommendations about testing methods.” See here.

The group released a 428-page report in February, describing its work and the tests it developed. As promised they are also making the data available. A request form for the data CD and a link to the report are here.

“They did a very competent job,” said Scott, who noted that their conclusions, as promised, were focused on the tests themselves. “Their conclusion was that you needed a high level of automation to conduct these tests.”

Damages
Scott reviewed the report and found the data in its charts was “pretty much consistent” with the Roberson tests, the tests done by the Technical Working Group (TWG) in 2011 that first clarified the levels of interference and the initial results of the Adjacent-Band Compatibility (ABC) Assessment now being conducted by the Department of Transportation (DoT).

“Basically what they’re showing is, you start transmitting high-power next to GPS, you’re going to damage national infrastructure,” Scott told Inside GNSS.

Scott’s perspective is in stark contrast to the interpretation put forth by Ligado, in a February 24 letter to the FCC. (Interestingly, the letter described the NASCTN project simply as a “Government study” and did not mention the firm’s sponsorship.)

“This comprehensive 428-page study that involved 1,476 hours of testing,” they wrote, validates the conclusion reached by the major GPS companies over the last 14 months: An LTE network operating within the specifications proposed in Ligado’s pending FCC applications will not harm the performance of GPS devices.”

Scott vehemently disagrees with the interpretation. “The report does not say that and the report actually provides quite a bit of evidence that (the network) will cause harm. How they reached this conclusion from this report is just beyond me because the data in the report very clearly shows damage.”

The difference in views appears to hinge on Ligado’s continued assertion that position error is the appropriate yardstick for interference.

“The government study found no impact on the position and timing accuracy of many GPS devices when exposed to mid-band LTE signals at significantly higher power than they would be under Ligado’s proposal,” the Feb. 24 letter said. The charts in the study, they continued, “illustrate that the study confirms Ligado’s prior submission in the record that a 1 dB-Hz decrease in the carrier-to-noise-density ratio (C/N₀) is not the appropriate standard for assessing harm to GPS receivers.”

Charts 6.24 and 6.28 from the study, however, show several of the receivers losing satellite lock as the signal power increases though it is not possible to determine which devices those are. The study lists the receivers tested, but it does so in such a way that specific results cannot be attributed to a specific piece of equipment.

Scott pointed out that the geographic impact of the network’s signals, based on the study’s data in Figure 6.24, could be substantial.

“So what kind of range is associated with that at the powers you’re talking about?” said Scott. “You’re talking about something of the order of 5 kilometers. So, you turn on a Ligado base station and 5 kilometers away there are high-precision receivers that are dying. And I don’t just mean degrading, I mean dying. Dead. Unable to operate.”

Scott explained that his conclusion is not based on NASCTN data, as NASCTN did not measure for such impacts, but on data from the TWG — the cadre of researchers who first looked at the LightSquared/GPS interference issue in 2011. Those researchers conducted tests at the same signal power that Ligado is now proposing, he said. To compare the two sets of data he annotated a chart based on TWG part 4 measured signal strength with boxes based on data from the NASCTN report. High-precision “receivers could experience total failure at ranges > 4 kilometers,” he said.

Scott noted also that there are reference receivers among the devices tested and that position error measurements would not reflect damaging interference to those receivers.

“The measure of a reference receiver is not its location performance; it knows exactly where it is,” Scott said. “The measure of its performance is how many satellites it’s tracking and how accurately it’s tracking the satellites. Looking through the test results I’m seeing all kinds of degradation. I’m seeing situations where they just flat out kill the receiver.”

The Adjacent-Band Assessment
The results from what, potentially, could be the most impactful study, however, are yet to come.

The ABC Assessment is a two-phase effort to determine the power limits by frequency, that is the interference tolerance masks (ITM) needed to protect both existing and future GPS receivers. The ABC Assessment is looking at a range of receivers and was developed with significant community input, including input from Ligado, which has been unhappy with the fact that it relies on C/N₀.

The first round of results, released in October, confirmed that cell phone receivers were the least sensitive and high-precision receivers the most sensitive, which tracks with earlier tests. Interestingly the masks developed for certified aviation receivers, which would seem likely to be restrictive, were not enough to protect other kinds of receivers. See here.

The ABC Assessment is “very conservative and therefore going to be pretty difficult [for Ligado] in terms of the power limits that are going to be imposed on the transmitters,” said Farrar. “…Obviously Ligado would, presumably, hope the FCC would just agree to the limits that Ligado proposed previously. But [Ligado’s proposed limits] are not likely to be viewed particularly positively [if the DoT is much more conservative].”

As of press time, the second round of data from the ABC Assessment was set to be released March 30. More details on the Global Positioning System Adjacent Band Compatibility Assessment can be found online here.

The post Ligado Test Results Roll In appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

1996 soccer game in the Midwest, (Rick Dikeman image)

Nouméa ground station after the flood

A pencil and a coffee cup show the size of NASA’s teeny tiny PhoneSat

Bonus Hotspot: Naro Tartaruga AUV

Pacific lamprey spawning (photo by Jeremy Monroe, Fresh Waters Illustrated)

“Return of the Bucentaurn to the Molo on Ascension Day”, by (Giovanni Antonio Canal) Canaletto

The U.S. Naval Observatory Alternate Master Clock at 2nd Space Operations Squadron, Schriever AFB in Colorado. This photo was taken in January, 2006 during the addition of a leap second. The USNO master clocks control GPS timing. They are accurate to within one second every 20 million years (Satellites are so picky! Humans, on the other hand, just want to know if we’re too late for lunch) USAF photo by A1C Jason Ridder.

Detail of Compass/ BeiDou2 system diagram

Hotspot 6: Beluga A300 600ST

Tracking illegal logging in Romania, autonomous mining, ancient calendars and Canadian cows

Tracking illegal logging in Romania, autonomous mining, ancient calendars and Canadian cows

1. Ghost trucks tracked
Romania
√ Every day on Romania’s highways, “ghost trucks” slip by unnoticed. Digital records show the vehicles, loaded with timber, coming from verified logging sites in the Romanian forest. But the GPS data associated with the records reveal they actually have much more random origins: from cornfields, to cemeteries, to California. It’s part of an illegal logging industry the government has said removes an estimated 141 million cubic feet of timber each year for the country’s old growth forests, some of the last in Europe. But a new, high-tech solution from Romania’s Ministry of the Environment is designed to counteract illegal logging by putting as much information as possible in the hands of the public using satellite imagery and GPS tracking. The new system, launched in December, is called Inspectorul Padurii, which means “Forest Inspector.” It works by combining images from three different satellites, taken at least once every five days. This information is used to help spot illegal logging.

2. Mining autonomously
Australia
√ Trucks the size of a small two-story house without a driver or anyone else on board. Mining company Rio Tinto has 73 of these titans hauling iron ore 24 hours a day at four mines in Australia’s Mars-red northwest corner. At one, known as West Angelas, the vehicles work alongside robotic rock drilling rigs. The company is also upgrading the locomotives that haul ore hundreds of miles to port — the upgrades will allow the trains to drive themselves, and be loaded and unloaded automatically. Rio Tinto intends its automated operations in Australia to preview a more efficient future for all of its mines — one that will also reduce the need for human miners. The rising capabilities and falling costs of robotics technology are allowing mining and oil companies to reimagine the dirty, dangerous business of getting resources out of the ground. Rio Tinto uses driverless trucks provided by Japan’s Komatsu. They find their way around using precision GPS and look out for obstacles using radar and laser sensors.

3. Calendar Rock
Italy
√ Italian archaeologists have found an intriguing Stonehenge-like “calendar rock” in Sicily. Featuring a 3.2-foot diameter hole, the rock formation marked the beginning of winter some 5,000 years ago. The holed Neolithic rock was discovered Nov. 30, 2016 on a hill near a prehistoric necropolis six miles from Gela, on the southern coast of Sicily, by a team who was surveying some World War II-era bunkers. Using a compass, cameras and a video camera mounted to a GPS-equipped drone, archaeologist Giuseppe La Spina and colleagues carried out a test in December at the winter solstice. The idea was to find out if the rising sun at solstice aligned with the distinct hole in the rock feature. According to La Spina, the experiment was “a total success.” At least two other holed stones have been found in Sicily in the past.

4. Cow Heard
Canada
√ In the mid-’70s, as a research scientist at the Melfort Research Station, Duane McCartney helped Saskatchewan Agriculture evaluate the first button-type electronic ear tags on their cows at the Pathlow pasture research project. At the time, he also had a big satellite remote sensing project to monitor pasture productivity, and would tell colleagues that the goal was to develop a system whereby he could sit in his office back in Melfort and monitor and remotely move the cows to different paddocks. They all laughed back then, but now it is a reality. There are some exciting innovations on the horizon for managing grazing operations, and recently Saskatoon was host to over 500 rangeland researchers and managers from 48 different countries at the International Rangeland Congress. The event featured over 500 presentations on all sorts of topics involving rangeland management. With the theme of “Managing the World’s Rangelands and Wild Lands in a HighTech World” it provided a forum for some very interesting capabilities of computers, cellphones, internet and satellite remote sensing for enhanced rangeland management.

The post GNSS Hotspots | January 2017 appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.