The Trimble AP+ Land is a comprehensive solution for land vehicle applications that is small enough to easily integrate into the most compact mobile mapping systems. It is also compatible with virtually any type of mapping sensor, according to the company, including single or multi-LiDAR systems, video cameras, photogrammetric and panoramic cameras and other similar sensors.

Configurable to meet the mapping, positioning and direct georeferencing (DG) accuracy demands of mapping and positioning applications in challenging GNSS signal environments, the Trimble AP+ Land solution features:

• Applanix IN-Fusion+ GNSS-aided inertial firmware with Trimble ProPoint GNSS positioning technology
• Dual embedded survey-grade GNSS chipsets that can receive multi-frequency and multi-constellation signals
• Dual custom designed inertial measurement units (IMUs)
• Distance measurement indicator (DMI)
• Compact size
• Low power consumption
• Optional RTK and Trimble CenterPoint RTX real-time correction service support
• Full integration and post-sales support through the Applanix Global support network

“We have taken the most advanced features of Applanix inertial and Trimble GNSS technology, and packaged them into a powerful compact and versatile solution optimized for mobile mapping and positioning applications,” said Joe Hutton, Applanix’s director of inertial technology, air and land products.

The Trimble AP+ Land OEM solution is supported by the Applanix POSPac MMS post-processing software, which features Trimble CenterPoint RTX post-processing for centimeter-level positioning globally without the need for base stations. These capabilities enable integrators to produce an efficient land mobile mapping system.

For LiDAR integrators, the Trimble AP+ Land OEM is compatible with the POSPac MMS LiDAR QC tools.  SLAM technology computes the IMU to LiDAR boresight misalignment angles and also adjusts the trajectory to achieve the highest level of georeferencing accuracy in the generated point cloud.

The post GNSS-Inertial Sensor Integrates with LiDAR, Video and Still Cameras for ADAS Testing and Fleet Monitoring 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.

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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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Table 1

Working Papers explore the technical and scientific themes that underpin GNSS programs and applications. This regular column is coordinated by Em. Univ.-Prof. Dr.-Ing. habil. Dr. h.c. Guenter W. Hein.

This is the first article in a series. For Part 2: Hybrid- and Non-GNSS Localization, see here.

Working Papers explore the technical and scientific themes that underpin GNSS programs and applications. This regular column is coordinated by Em. Univ.-Prof. Dr.-Ing. habil. Dr. h.c. Guenter W. Hein.

This is the first article in a series. For Part 2: Hybrid- and Non-GNSS Localization, see here.

Positioning (or localization) is a key component in many wireless devices and a key enabler and optimizer of many mobile applications, including transportation, smart cities, and ambient assisted living. For example, mobile wireless devices relying on a location component can be used as mobile assistants and wearable devices for the elderly, sick, or disabled, for traffic and environment monitoring, for green mobile crowd sensing, in crisis scenarios, for wildfire risk prediction, etc. (see I. Maglogiannis et alia and L. Skorin-Kapov et alia in Additional Resources). When the time dimension is added to the positioning information, we talk about user or device tracking.

To enable a large-scale uptake of the location-based and location-aware applications, one of the main barriers to overcome is finding solutions to the current vulnerabilities in wireless positioning. Such vulnerabilities exist with respect to the privacy, security, positioning reliability, robustness, and availability, especially in indoor environments, and to the acceptability and safety of tracking devices. Users are indeed, slowly, becoming aware of the potential vulnerabilities in making their minute-by-minute position known to the external world and legislation efforts all over the world are dedicated to build the legal frameworks covering tracking and location privacy (see L. Chen et alia and K. Pomfret). Operators and mobile manufacturers are collecting location-based data and possibly geo-tagged context information en masse from our mobile devices for the purpose of network and service optimization. Crowdsourcing, mobility sensing, and cloud storage processing are becoming default options. Many mobile devices can now be used as identifiers, and digital wallets and biometric data play a crucial role. Location is a key component in all of these aspects. A known location, or being able to fake a current location, could mean, in the near future, higher vulnerability to theft, privacy invasion, and increased stalking. Geo-located patterns can lead to the re-identification of individuals and thus could pose a risk to the right to a private life. All these vulnerabilities with respect to the acquisition, storage, and misuse of the users’ geospatial information are long-overlooked factors which need to be addressed in a systematic and dedicated manner. The promising potential of future prosperous wireless markets relying on some form of localization and geo-spatial information, such as Internet of Things (IoT), Industrial IoT (IIoT), 5G, Device-to-Device (D2D), or Vehicle-to-Anything (V2X) communications, means that the security, privacy, and transparency aspects in mobile positioning need to become a high priority in the world of mobile computing.

In traditional positioning approaches, such as those purely based on Global Navigation Satellite Systems (GNSS), the user device is a purely passive device, thus fully preserving the user’s privacy. Modern localization solutions, including those evolved from GNSS such as Cloud GNSS (C-GNSS) and Assisted GNSS (A-GNSS) involve smart processing of cloud-gathered data, inter-connectivity, and exchange of information between different stakeholders in the localization chain, and possibly geo-tagged content de-identification. Therefore, these are vulnerable to privacy breaches, whereas the user position is fully private in GNSS as its receiver acts only as a passive (receiving) device. This article sheds light on the challenges related to location privacy, emphasizing current user perception of location-based mobile applications, and discusses future research directions and solutions that can benefit the community at large.

Is Location Privacy Something We Should Worry About?
In order to better understand users’ concerns with regard to their location privacy and how much users would be willing to pay for preserving their location privacy, a Webropol web survey (see Additional Resources) was conducted from January to May 2017. The survey was initially built in English and then translated to Finnish and Romanian. The survey link was distributed on different social media channels (e.g., LinkedIn, Twitter, Facebook) and through various mailing lists in order to reach a wide audience with variable backgrounds. In total, 327 answers from respondents across 38 countries in four continents were obtained. 8.8% of the respondents did not answer the question about the country of residence. There were 208 answers in English, 79 in Finnish, and 40 in Romanian. The overall gender distribution is quite balanced: 46.3% male respondents, 49.4% female respondents, and 4.3% declined to state. The age and country distribution of respondents are shown in Figure 1, with Finland, Romania, and UK being the countries of residence for most of the respondents, and the majority of respondents being between 36 and 45 years old.

Figure 2 shows how the respondents are using their mobile phone’s navigation capabilities and Location Based Services (LBS) on their mobile devices. The left plot shows that the vast majority of users (86.6%) are using some form of navigation on their phone, among which 52% typically activate both the GNSS and the non-GNSS (e.g., WiFi and cellular) positioning engines on their phones when navigating. The center plot of Figure 2 describes how often a user reads the permissions before installing an LBS application on his/her phones. These permissions are more or less intrusive in terms of privacy, depending on the application provider and reading them already denotes some minimal concern with regard to the privacy of mobile data. The survey shows that 30.0% of the respondents always read the permissions, 35.49% only occasionally read the permissions, and 28.4% never read the permissions. A small amount of respondents (6.2%) did not know about these permissions.

The right plot of Figure 2 shows how many of the respondents allow the LBS provider to collect their location data. The vast majority of respondents (61.9%) allow their location information to be collected only if they cannot use a particular service otherwise, as is the case with many LBS providers, such as Google maps and HERE maps. 5.2% of respondents always allow the location application to collect the user location data and 9.6% of respondents allow the location application to collect such data from time to time, independently of whether or not the LBS application could have been used in “private” mode (i.e., no data collection). 23% of respondents answered that they had never allowed an application to collect their location data. However, one could also infer that it might be unclear for some users whether or not a certain LBS application collects location data and sends it to the cloud. This comes as a conclusion when comparing the left plot of Figure 2, where only 13.6% of the respondents wrote that they do not use any location engine on their phone, with the right plot of Figure 2, where 23% of respondents say that they never allow an application to collect their location data. Nevertheless, one has to keep in mind that the vast majority of current LBS mobile applications cannot run unless the user allows the application to collect his/her location data.

Figure 3 compares the level of concern of users with respect to their location privacy with other types of personal digital data, such as emails, documents, calls, phone contacts, or images/videos. If we look at the “Very high concern” bars, clearly the users are much more concerned with protecting the privacy of all other types of personal digital data than protecting the location privacy. However, “High” and “Moderate” concerns bars are rather similar for different types of data, which shows that users have significant concerns regarding the privacy of all their personal data, location data included. As for the “No concern” bars, privacy of pictures and videos are least worrisome to those in the survey, with 12.6% having no concern for the privacy of these items. For location privacy, 7.3% had no concern, 14.4% had little concern, 19% moderate concern, 24.5% high concern, and 29.4% very high concern.

How these concerns translate also in a willingness to pay extra for location privacy can be seen in Figure 4. Clearly, the vast majority of users (61.9%) are not yet ready to pay anything extra for a privacy-preserving location engine. It is interesting to see that, among those who are interested in paying something (23.2% of the total respondents), the majority (60.9% of the respondents willing to pay something) would opt to pay up to 15% more compared to their current monthly mobile fee, and no respondent opted to pay more than 20% of the current monthly fee.

The survey findings show that there is already a reasonable awareness about location privacy challenges and that such awareness could be capitalized upon to some extent in business, by offering users more privacy-aware location solutions. The next sections will focus first on some aspects regarding granularity of location estimation, and then on GNSS location technologies categories, and will point out if and how such technologies can better support location privacy.

Granularity of Location Estimates for Various LBS
When talking about location privacy, one refers to the capacity of preventing any third parties to learn anything about a device location in space and time. There is thus a quadruplet (x, y, z, t) characterizing the location, where x, y, z are the spatial coordinates of the mobile device and t is the time at which that location is valid. When time is also known, we often talk about the user or device tracking.

There are two ways of defining the granularity of a location estimate: one is from the point of view of an attacker and it refers to the accuracy level at which the attacker can detect the location information; the other is from the user’s point of view, and it refers to the Quality of Service (QoS) received from a Location Service Provider (LSP), knowing that there is an inherent tradeoff between preserving his/her own location privacy via, for example, some cloaking or obfuscation mechanisms, and the QoS of the LBS. For example, let’s assume that the user’s true position at time t is (x, y, z), but the location information sent to the LBS and/or accessed by an attacker is (x + Δx, y + Δy, z + Δz). Then, the location granularity g in this case is defined as

g = √Δx² + Δy² + Δz²,

which is basically the distance uncertainty in the location estimate.

Table 1 (see inset photo, above right) gives examples of how an attacker can make use of the user location, if the user location is known with a certain granularity. The last column also shows positive examples of how the location information of a certain granularity can serve the user. Typically, the location needs to be known at several moments in time, ranging from a few hours to several months, in order for an attacker to be able to act upon the knowledge, but sometimes even the knowledge of as little as four different locations in time can lead to personal identification (see De Montjoye et alia in Additional Resources). As shown in Table 1, while one may be completely unconcerned if his/her location is known within a kilometer of error, this might be enough for an attacker to establish if a family is away from their home and to organize a house burglary. The examples of attacks shown in all rows above the current row are also applicable to the current row. For example, if the location is known within a few tens of meters from the actual position, house burglary, car thefts, or stalking are also potential threats, in addition to terrorism or disclosures of unwanted personal information, which are enabled by a more precise location known to an attacker.

Location Privacy in GNSS-Based Positioning Cloud GNSS
In the coming years, the development of new GNSS-based applications will play a leading role in the context of urban environments, i.e., Smart Cities, where almost every object or device such as urban furniture or wearable items can be connected between themselves, i.e., Machine to Machine (M2M) or D2D, and to the internet. In this sense, IoT applications have triggered the use of GNSS technologies for retrieving the Position, Velocity, and Timing (PVT) of the devices. Nevertheless, GNSS was designed for outdoor applications, and its performance gets truncated in urban working conditions. Moreover, IoT devices cannot implement advanced computational tasks due to constraints on low energy consumption, thus hindering the use of GNSS in harsh working conditions such as urban canyons, indoors, etc. Computational constraints are not only circumscribed to GNSS-based IoT devices, but also to conventional GNSS receivers providing advanced features such as multi-constellation processing, signal authentication, or threat detection (e.g., interference or propagation effects such as multipath or NLOS).

To overcome this hurdle, the Cloud GNSS concept has recently been proposed as a disruptive approach for solving most of the current limitations of conventional GNSS receivers (see Additional Resources). In this paradigm, the GNSS signal processing tasks traditionally carried out in on-chip GNSS modules at the user terminal, are now relocated in a cloud server, as illustrated in Figure 5, where on-demand scalable computing capacity in terms of data storage and processing power is available. Thanks to this availability, the energy consumption and computational power required by the user terminal is significantly reduced, since its main function is now to gather raw GNSS samples and transfer them to the cloud. Thanks to the computing capacity provided by the cloud, sophisticated GNSS signal processing techniques can easily be performed, thus providing a wider range of use cases where the GNSS sensor can effectively operate. For instance, Cloud GNSS can be used in liability-critical and safety-critical applications, where the use of conventional GNSS receivers faces some limitations due to the stringent requirements imposed on the user terminal in terms of integrity, continuity and, in the future, authentication.

The transfer of information from the user terminal to the cloud may raise some concerns on the potential vulnerabilities of cloud GNSS signal processing in terms of privacy and security. From a high-level perspective, we can identify three different categories of vulnerabilities, explained below: i) at the user-to-cloud communication link; ii) on the cloud storage of personal digital data, such as identifiers or GNSS raw samples; iii) on the computation, and therefore knowledge, of users’ location by third-parties, for example at the Location Based Service Provider (LBSP) side.

User-to-Cloud Communication
During the transmission of raw GNSS samples from the user’s device to the cloud server, personal data may eventually be intercepted by attackers. Location may also be known by the service provider of the network infrastructure due to the identifier each device holds, e.g., IP or MAC address or International Mobile Station Equipment Identity (IMEI). However, communication privacy and security is already provided by wireless infrastructures through secure communication protocols and standards, e.g., user access authentication implemented in Long Term Evolution (LTE) or Narrow Band Internet of Things (NB-IoT). Hence, the security and privacy of the user-to-cloud communication link is achieved with state-of-the-art wireless communication standards. Besides that, some cloud providers also offer secure platforms to connect users’ devices with the cloud. For instance, Amazon Web Services (AWS) offers an IoT platform, which already provides traffic encryption over Transport Layer Security (TLS) by using different cryptography standards such as X.509 or the Signature Version 4 Signing Process (SigV4).

Cloud Storage of Personal Digital Data
Customers may worry about the security involving the raw GNSS samples and personal digital data they upload to the cloud, due to the possibility of it being read or analyzed by third-parties (or attackers) and thus being used in an unauthorized manner. Nine critical threats to cloud security are identified by the Top Threats Working Group (Additional Resources): data breaches, data loss, account hijacking, insecure APIs (Application Program Interface), denial of service, malicious insiders, abuse of cloud services, insufficient due diligence, and shared technology issues. To prevent many of these threats, current cloud providers such as AWS, Microsoft Azure, and Google Cloud provide high-security systems with ISO 27001 certification, thus assuring confidentiality, integrity, and availability. With regard to the stored data, cloud platforms do not distinguish between personal data and any other type of data. Therefore, by using certified cloud platforms, the security of personal data, which in this case would be the raw GNSS samples file, the device location and any other stored personal data, would be guaranteed. Users shall realize that the security policies of a cloud service may change depending on the legislation of the country in which the cloud server is allocated, and hence personal digital data may be accessed by the government.

User’s Location Calculation by Third-Parties
Device anonymization is needed when the user’s location is known to a third-party, either when the location is calculated by the cloud GNSS platform or when it is used by some LBS. If not, the cloud and the LBS server might know who the devices’ owner is, and use the personal data and location for their own benefit. For LBS, a k-anonymization model to deal with location privacy is presented by B. Gedik and L. Liu. In this approach, an anonymity server decrypts the data transferred from the device to the LBS and removes all the related identifiers (e.g., IP or MAC address, device, or customer identifier). Next, the location information is disrupted by means of spatio-temporal cloaking (i.e., hiding the true location information in a wide spatio-temporal area), and finally, the anonymized location is sent to the LBS server. Note that this approach may perturb the quality of service, and thus a tradeoff between the QoS and location privacy is faced.

Another alternative is to assign a random identifier to every device, which is then changed after a fixed time such as minutes, hours, or days depending on the application, in order to facilitate the anonymization. In this context, hash-based ID variation (see Additional Resources) can be used for enhanced location privacy. This process is often accomplished through two different and independent entities, the first one (e.g., a certification body) being in charge of randomly assigning identifiers to users, and the second one (e.g., the LBSP) being in charge of the exploitation of the randomized data. This scheme guarantees that the entity making use of the data has no access to the mechanism whereby the identifier was generated and assigned to the user. In this manner, users have a time-varying identifier with limited lifetime that thus cannot be tracked for a long period of time. Clearly, the shorter the identifier lifetime, the better the user privacy, since we prevent inferral of user identification via behavior pattern analysis.

In conclusion, there are many protection schemes that can be used to solve the potential vulnerabilities of cloud GNSS positioning in terms of location security and privacy. When it comes to strengthening the privacy requirements of the user’s location, this often translates into a tradeoff between privacy and QoS.

Assisted GNSS
In order to position itself using the signals from navigation satellites, the GNSS receiver needs to know the precise time and orbital parameters to compute the positions of the satellites. The GNSS satellites broadcast this information in their navigation messages. However, decoding the orbital information from navigation messages takes 30 seconds in good signal conditions, which is a significant Time-To-First-Fix (TTFF), i.e., delay in the starting of positioning. This time may be much longer in environments dense with buildings or foliage where these obstacles attenuate the satellite signals. If the signal power decreases further, the receiver cannot decode the navigation data even if it is still able to make the ranging measurements. In this case, without information on satellite orbits and precise time, the measurements are useless for the receiver and it cannot compute its position.

In A-GNSS, the functionalities of a GNSS receiver are enhanced through terrestrial communication networks to shorten the TTFF and to improve the sensitivity of the receiver, i.e., to allow positioning with weaker satellite signals (J. Syrjäinne; F. Van Diggelen, Additional Resources). In A-GNSS, the missing information is provided to the receiver by a server that is connected to the receiver that has good visibility to the satellites (Figure 6). In addition to the orbital information and time, the A-GNSS can also deliver the Differential GNSS (DGNSS) corrections which allow improvements of positioning accuracy even to the one meter level.

Two architectures were proposed for A-GNSS where the roles of the user terminal (mobile station, MS) and the server in the network are different. In MS-based A-GNSS (MS-Based Network-Assisted) the user receives assistance data from the server and makes the ranging measurements, possible DGNSS corrections, and positioning calculations by itself. In MS-assisted A-GNSS (MS-Assisted Network-Based) the user terminal makes the ranging measurements and sends them to the server. The server applies the possible DGNSS corrections to the measurements, computes the position, and sends it back to the user. To assist the user terminal in the positioning measurements, the server sends a small set of assistance data to the user to enable fast signal acquisition.

In MS-based A-GNSS, in good signal conditions the user terminal can also position itself without assistance from the server. That is to say, the network connection is not necessary. In MS-assisted A-GNSS, the positioning of the user terminal always requires two-way communication with the server. The best achievable accuracy in both A-GNSS modes is defined by the DGNSS, which allows accuracies on the level of one meter (see Additional Resources). However, both modes are susceptible to multipath and NLOS, and therefore the accuracy is not always as good as in clear LOS. Actually, in an MS-based approach, the positioning accuracy may deteriorate to hundreds of meters when assistance is needed due to bad signal conditions. When the satellite signal levels drop very low, e.g., in underground settings, the user devices also cannot make the measurements, and both A-GNSS modes fail.

While both MS-based and MS-assisted architectures require point-to-point communication, either in the control plane of a cellular network or in the user plane of a wireless internet, only in MS-assisted approach does the user terminal reveal its accurate position to the server. The functionalities of the current Cloud GNSS are very similar to MS-assisted A-GNSS, therefore the privacy threats are also similar. For A-GNSS, secure architectures exist (L. Wirola et alia), e.g., the Open Mobile Alliance Secure User Plane Location Protocol (OMA SUPL) which provides security and authentication services using Generic Bootstrapping Architecture (3GPP GBA) (please see Additional Resources).

Conclusions
Our studies shed more light on users’ perception of their location privacy and on the privacy threats and solutions in modern wireless localization. We learned that, in general, users are not yet particularly aware of location privacy threats and most would not be willing to pay much or anything for private or passive positioning. In addition, privacy of localization is not yet fully solved in many state-of-the-art GNSS localization systems, such as Cloud GNSS and Assisted GNSS.

Acknowledgements
The authors express their warm thanks to the Academy of Finland (Project 303576) for its financial support for this research work.

Additional Resources
[1] 3GPP TS 33.220 Generic Bootstrap Architecture
[2] Chen, L., Thombre, S., Järvinen, K., Lohan, E. S., Korpisaari, P., Kuusniemi, H., Leppäkoski, H., Honkala, S. , Bhuiyan, M. Z. H., Ruotsalainen, L., Ferrara, G. N., Bu-Pasha, S., “Robustness, Security, and Privacy in Location-Based Services for Future IoT,” IEEE Access, Vol. 5, pp. 8956-8977, 2017
[3] De Montjoye, Y. A. , Hidalgo, C. A., Verleysen, M., and Blondel., V. D., “Unique in the Crowd: The Privacy Bounds of Human Mobility,” Scientific Reports 3, Article number: 1376, 2013
[4] Gedik, B. and Liu, L., “Location Privacy in Mobile Systems: A Personalized Anonymization Model,” Proceedings of the 25th IEEE International Conference on Distributed Computing Systems, ICDCS, pp. 620-629, June 2005
[5] Gschwandtner, F. and Schindhelm, C. K., Spontaneous Privacy-Friendly Indoor Positioning using Enhanced WLAN Beacons, 2011
[6] Henrici, D. and Muller, P., “Hash-based Enhancement of Location Privacy for Radio-Frequency Identification Devices using Varying Identifiers,” Proceedings of the 2nd IEEE Annual Conference in Pervasive Computing and Communications Workshops, pp. 149-153, March 2004
[7] Li, M., Zhu, H., Gao, Z., Chen, S., Ren, K., Yu, L., and Hu, S., “All Your Location are Belong to Us: Breaking Mobile Social Networks for Automated User Location Tracking,” Proceedings of MobiHoc, ACM, pp. 43-52 2014
[8] Lucas-Sabola, V., Seco-Granados, G., López-Salcedo, J. A., García-Molina, J. A., and Crisci, M., “Cloud GNSS Receivers: New Advanced Applications Made Possible,” Proceedings of the International Conference in Localization and GNSS (ICL-GNSS), pp. 1-6, June 2016
[9] Maglogiannis, I., Kazatzopoulos, L., Delakouridis, K., and Hadjiefthymiades, S., “Enabling Location Privacy and Medical Data Encryption in Patient Telemonitoring Systems,” IEEE Transactions on Information Technology in Biomedicine, Vol. 13, No. 6, pp. 946-954, November 2009
[10] Mascetti, S., Bertolaja, L., and Bettini, C., “A Practical Location Privacy Attack in Proximity Services,” 2013 IEEE 14th International Conference on Mobile Data Management, Milan, pp. 87-96, 2013
[11] Misra, P. and Enge, P., Global Positioning System – Signals, Measurement and Performance, 2nd ed., Ganga-Jamuna Press, ISBN 0-9709544- 1-7, 2006
[12] OMA Secure User Plane Location 1.0, OMA-ERP-SUPL-V1_0-20070615-
[13] Pomfret, K., “Geolocation Privacy – Implications of Evolving Expectations in the United States,” Inside GNSS, September/October 2016
[14] Skorin-Kapov, L., Pripužić,K., Marjanović, M., Antonić, A., and Žarko, I. P., “nergy Efficient and Quality-Driven Continuous Sensor Management for Mobile IoT Applications,” 10th IEEE International Conference on Collaborative Computing: Networking, Applications, and Worksharing, Miami, FL, pp. 397-406, 2014
[15] Syrjärinne, J., Studies of Modern Techniques for Personal Positioning, Ph.D. Dissertation, Tampere University of Technology, 2001
[16] Top Threats Working Group, The Notorious Nine: Cloud Computing Top Threats in 2013, Cloud Security Alliance, 2013
[17] Van Diggelen, F., A-GPS : Assisted GPS, GNSS, and SBAS, Artech House, 2009
[18] Webropol web survey tool
[19] Wirola, L., Laine, T. A., and Syrjärinne, J., “Mass-Market Requirements for Indoor Positioning and Indoor Navigation,” Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Zürich, Switzerland, 2010

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INTERGEO, the 2017 European geodesy, geoinformation and land management conference and trade fair, will take place at  Messe Berlin, South Entrance in Berlin, Germany on September 26, 27 and 28.

INTERGEO, the 2017 European geodesy, geoinformation and land management conference and trade fair, will take place at  Messe Berlin, South Entrance in Berlin, Germany on September 26, 27 and 28.

The  annual three-day event covers the entire spectrum of processing, using and analyzing geodata and attracts nearly 1,500 conference participants and 17,000 visitors from 107 countries. For information about the conference, contact Christina Gunesch at the email address below.

The topics are:
Cloud
Surveying
Geospatial 4.0
Photogrammetry
GNSS
UAV
Big Data
GIS Solutions
Laser scanning
Building INformation Modeling
Smart City

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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

Highest altitude fix for a GPS signal, GNSS timing signals and hacking the Grid, Eagles act as drone countermeasures and rumors of a GNSS-nano-chip contributes to cash crisis in India

Highest altitude fix for a GPS signal, GNSS timing signals and hacking the Grid, Eagles act as drone countermeasures and rumors of a GNSS-nano-chip contributes to cash crisis in India

1. GUINNESS WORLD RECORD!
Outer Space
√ NASA’s Magnetospheric Multiscale Mission (MMS) earned a Guinness World Record for the highest altitude fix of a GPS signal in November when the four satellites were on their elliptical orbit 43,500 miles from Earth. When the MMS satellites are closest to us down below, they travel up to 22,000 miles per hour, making them the fastest known operational use of a GPS receiver. The four satellites fly in a tight flying formation using precise tracking systems that depend on GNSS.

• (November 4, 2016) NASA.gov: NASA’s MMS Breaks Guinness World Record

2. HACKING THE GRID?
Redlands, California and Idaho Falls, USA
√ Those delicate GNSS timing signals crucial to the synchrophasor systems on the electrical power grid keep cybersecurity folks awake at night. But, if hackers intrude on the grid, turning the lights out could be the least of our problems. Researchers from California’s Esri and the Department of Energy’s Idaho National Laboratory are using GIS to identify the likely ripple effects — from the spread of malware to water system failure to issues for first responders. In a Christian Science Monitor article about the project, the deputy director of the National Geospatial Intelligence agency said of the intersection of digital and physical worlds: “Too many people think that cyber is its own domain and quite frankly, everything resolves to physical.”

• (November 16, 2016) The Christian Science Monitor: If hackers cause a blackout, what happens next?

3. GOTCHA!
The Hague, Netherlands
√ To the list of drone countermeasures, add “eagles” along with “shoot it” and “jam its sensors.” Law enforcement in the Netherlands has partnered with a raptor-training company to teach eagles to identify drones intruding illegally in congested or secure areas, snatch them out of the sky and fly them somewhere away from the public. In theory, it promises significantly less collateral damage than other methods. The birds are rewarded with a piece of meat to make up for the tastelessness of drone and the police say the feathered hunters succeeded 80 percent of the time during the trial period.

• Guard From Above FAQ

4. FOLLOW THE MONEY?
New Delhi, India
√ India is a cash-based economy and fake currency notes and tax avoidance run rampant. Early in November, the government withdrew two often-counterfeited high-value notes that comprise 80 percent of the cash in circulation and released a new 2000-rupee bill (that’s about US$30). This has not gone well. In addition to making it tough for people to exchange money, the move inspired rumors that the new note contained a secret mini nano-GPS chip that could reveal stashes of no-doubt undeclared cash buried as deep as 393 feet below ground. Rubbish, said the Reserve Bank of India: “Such a technology does not exist at this moment in the world.”

• (November 10, 2016) The News Minute: RBI rubbishes rumours of GPS tracking chip in Rs 2000 note
• (November 10, 2016) India.com: Rs 2000 currency notes issued by Reserve Bank of India will have no GPS tracking chip, confirms Arun Jaitley

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The 10th International Symposium on Mobile Mapping Technology (MMT2017) will take place from May 6 – 8, 2017 at the Conrad Cairo Hotel, on the river Nile in Cairo, Egypt.

Online registration is available. Early bird rates end January 1, 2017.

The 10th International Symposium on Mobile Mapping Technology (MMT2017) will take place from May 6 – 8, 2017 at the Conrad Cairo Hotel, on the river Nile in Cairo, Egypt.

Online registration is available. Early bird rates end January 1, 2017.

The MMT2017, “Mobile Mapping for Sustainable Development”, offers a forum for research and development in mobile mapping technology, systems and applications. It will highlight recent and significant advances in research and development in Mobile Mapping Technology.

The conference will also provides a platform for international scholars, graduate students, future scientists and industrial sectors to learn exchange knowledge and experiences of applying up-to-date mobile mapping technologies for sustainable development.

Topics this year include:

• Navigation, Sensors and Positioning
• Mobile Mapping Applications
• Digital Imaging and Remote Sensing Systems
• Smart Collaborative Platforms and Logistics
• Big Data and Risk Management

The Keynote Speakers is Christian Heipke (ISPRS President): “Geospatial Information: State of the Art and Future Trends”

The event is hosted by the Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Egypt.

The Symposium has become the largest international conference dedicated to mobile mapping technology and applications. The event is jointly sponsored by the International Society of Photogrammetry and Remote Sensing (ISPRS), International Association of Geodesy (IAG), International Cartographic Association (ICA) and the International Federation of Surveyors (FIG).

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Topcon Positioning Group will distribute Intel Corporation’s Falcon 8+ System, a V-shaped, eight-rotor unmanned aerial system (UAS) in North American markets.

Announced at Intergeo 2016 in Hamburg, Germany, the Intel Falcon 8+ features triple-redundant AscTec Trinity autopilot capability and has full electronic system redundancy, the company said. It also includes the Intel Cockpit ground control and Powerpack smart battery system.

Topcon Positioning Group will distribute Intel Corporation’s Falcon 8+ System, a V-shaped, eight-rotor unmanned aerial system (UAS) in North American markets.

Announced at Intergeo 2016 in Hamburg, Germany, the Intel Falcon 8+ features triple-redundant AscTec Trinity autopilot capability and has full electronic system redundancy, the company said. It also includes the Intel Cockpit ground control and Powerpack smart battery system.

The Intel Falcon 8+ foray into North American markets is a good one, particularly because Topcon has one of the largest UAS reseller and support networks, said Anil Nanduri, Intel Corporation’s UAV Group manager.

"The Intel Falcon 8+ System expands on the success of our rotary-wing UAS offering," said Eduardo Falcon, Topcon GeoPositioning Solutions Group executive vice president and general manager.

Falcon said the UAS offers high-precision GNSS, weight-to-payload ratio, stability in harsh conditions, and is easily exchangeable. Other features include a dual-battery system, comprised of Intel Powerpack smart batteries, which include automatic balancing, storage mode, charging and LEDs that display remaining battery life to meet airline shipping requirements, the company said.

In addition, the Intel Cockpit ground control is water resistant, so operators to plan and execute complex missions with the integration of a single hand, flight control joystick, the company said. The Intel Cockpit ground control also features an Intel-based tablet and supports low-latency digital video links, the company said.

Some Intel Falcon 8+ applications include providing precision data for inspection and monitoring, survey, and mapping applications.

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Terrebonne, Canada-based Eos Positioning Systems has released its Arrow Gold Bluetooth GNSS receiver. Arrow Gold is the first iOS, Android, and Windows Bluetooth GNSS receiver to work with GPS, Glonass, Galileo, BeiDou, Quasi-Zenith Satellite System (QZSS), and the Atlas correction service, the company said.

The palm-sized Arrow Gold receiver provides centimeter-level accuracy on iOS, Android, and Windows devices. Arrow Gold, which costs $4,395, works with such data collection systems as Esri’s Collector/Survey123 and others.

Terrebonne, Canada-based Eos Positioning Systems has released its Arrow Gold Bluetooth GNSS receiver. Arrow Gold is the first iOS, Android, and Windows Bluetooth GNSS receiver to work with GPS, Glonass, Galileo, BeiDou, Quasi-Zenith Satellite System (QZSS), and the Atlas correction service, the company said.

The palm-sized Arrow Gold receiver provides centimeter-level accuracy on iOS, Android, and Windows devices. Arrow Gold, which costs $4,395, works with such data collection systems as Esri’s Collector/Survey123 and others.

Arrow Gold also features a real-time kinematic (RTK) capability, for poor cellphone coverage areas, called SafeRTK. The SafeRTK feature uses satellite corrections to work when the user’s RTK network connection is lost, even in urban areas, the company said.

SafeRTK works when wireless coverage fails, allowing users to achieve centimeter-accuracy for as much as 20 minutes, free of charge, the company said.

Arrow Gold includes a rechargeable battery pack, weighs less than a pound, is waterproof, and works in rugged environments, the company said. The unit’s Bluetooth radio stays connected to a mobile device as far as 1,000 meters away.

Arrow Gold will be marketed to such markets as GIS, environmental, agriculture, electric/gas/water/telecom utilities, surveying, machine control, and governments.

Arrow Gold features include:

• Supports GPS, GLONASS, Galileo, BeiDou, QZSS.
• Triple-Frequency L1/L2/L5.
• 1-centimeter RTK real-time accuracy.
• Long-range RTK Baselines up to 50 kilometers.
• SafeRTK for poor cell coverage areas.
• Worldwide satellite correction service.
• 100 percent iOS, Android, and Windows compatibility.

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The unit, introduced at the InterGEO trade fair in Hamburg, Germany, uses Trimble’s CenterPoint RTX subscription service. The Trimble CenterPoint RTX service allows land, air, marine, and unmanned aerial vehicle (UAV) platforms to achieve centimeter-level accuracy within one hour after data collection with just an internet connection, the company said.

Applanix has released its POSPac MMS 8 GNSS-aided inertial post-processing software for georeferencing data collected from cameras, LIDARs, multi-beam sonars, and other sensors on mobile platforms.

The unit, introduced at the InterGEO trade fair in Hamburg, Germany, uses Trimble’s CenterPoint RTX subscription service. The Trimble CenterPoint RTX service allows land, air, marine, and unmanned aerial vehicle (UAV) platforms to achieve centimeter-level accuracy within one hour after data collection with just an internet connection, the company said.

The new software allows UAVs to map inaccessible regions that have no existing continuously operation reference stations (CORS), without having to deploy local base units, the company said. POSPac MMS 8 also features quality control software that can be used in the field for GNSS observations to ensure accurate specifications can be met before leaving the area, the company said.

POSPac MMS 8 will be available worldwide in the fourth quarter of 2016 through the Applanix sales channel, the company said.

Trimble CenterPoint RTX, which computes centimeter-level positions based on satellite orbit and clock information, combines real-time data from a global reference station infrastructure with positioning and compression algorithms, the company said. Trimble CenterPoint RTX service is available in six or 12-month subscriptions.

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