Starting with this SDX update is an upgraded paradigm for managing receiver antennas: SDX now supports the management of multiple vehicle antennas within a single scenario. Antennas can now be defined, named, and exported as antenna files that can be re-imported back into other scenarios. This handy feature will speed up antenna reuse and multi-scenario workflows for users managing numerous antenna models in GNSS simulation scenarios.

The new antenna model UI maintains the previous SDX paradigm whereby antenna patterns can be defined for gain and phase offsets.

Moreover, SDX now provides a simple and powerful sequencer for switching from one antenna pattern to another at specific times during a scenario. The antenna sequencer is said to be simple to use and will be useful for those who need to toggle antennas at various points in the simulation to replicate a real-world scenario.

As with all new features added by Skydel, the SDX API is updated—and documented—to reflect these latest changes, and these new features can be used in the programming of your automated scenarios.

IQ File as a Jammer Type
In addition, with release 18.10, users can now add IQ File playback as a jammer type in SDX. This feature, combined with the IQ File generation capability already available in SDX, opens a new range of possibilities.Users who already have the advanced jamming option installed and are eligible for this SDX upgrade can benefit from this new feature immediately by upgrading to SDX release 18.10.

Just One of Many Releases
So far in 2018, SDX has seen the support of anechoic chambers, the support for multiple GPUs, the addition of SBAS support among many other new features in the summer release, and now SDX release 18.10 that brings a new way to manage and sequence vehicle antennas.

According to Skydel, more announcements are just around the corner. New custom simulation solutions based on SDX are coming soon, along with new features and GNSS constellation support.

The post SDX 18.10 Now Available From Skydel 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

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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Congestion chokepoints hurt the economy and the environment, so the ATRI uses GPS data collected each year to help identify times and locations that truckers will want to avoid.

The American Transportation Research Institute (ATRI) released on January 25 its annual list highlighting the most congested bottlenecks for trucks in America.

Congestion chokepoints hurt the economy and the environment, so the ATRI uses GPS data collected each year to help identify times and locations that truckers will want to avoid.

"Trucks move 70% of the nation’s goods, so knowing where there are kinks and slowdowns in the system is important for motor carriers and our professional drivers, making this analysis a key tool for identifying where and when to route our trucks to avoid congestion," said Prime Inc. President and CEO Robert Low.

The 2017 Top Truck Bottleneck List assesses the level of truck-oriented congestion at 250 locations on the national highway system. The analysis, based on truck GPS data from 600,000+ heavy duty trucks, uses several customized software applications and analysis methods, along with terabytes of data from trucking operations to produce a congestion impact ranking for each location. The data is associated with the FHWA-sponsored Freight Performance Measures initiative. The locations detailed in this latest ATRI list represent the top 100 congested locations.

For the second straight year, Atlanta’s "Spaghetti Junction," the intersection of Interstates 285 and 85 North is the most congested freight bottleneck in the country. The rest of the Top 10 includes:

2.   I-95 at State Route 4 in Fort Lee, New Jersey
3.   I-290 at I-90/94 in Chicago
4.   I-65 at I-64/71 in Louisville, Kentucky
5.   I-71 at I-75 in Cincinnati
6.   SR 60 at SR 57 in Los Angeles
7.   SR 18 at SR 167 in Auburn, Washington
8.   I-45 at US 59 in Houston
9.   I-75 at I-285 North in Atlanta
10. I-5 at I-90 in Seattle

"With President Trump expected to press for significant long-term infrastructure spending, this ATRI analysis should be a key guide for deciding what projects are worthy of funding," said American Trucking Associations President Chris Spear. "Ensuring the safe and efficient movement of goods should be a national priority and this report draws attention to the places where our highway network needs improvement in order to meet that goal."

For access to the full report, including detailed information on each of the 100 top congested locations, click here.

ATRI is the trucking industry’s 501c3 not-for-profit research organization. It is engaged in critical research relating to freight transportation’s essential role in maintaining a safe, secure and efficient transportation system.

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Thalwil, Switzerland-based u-blox has launched the ZOE-M8G, an ultra-compact GNSS receiver module, especially designed for markets where small size, minimal weight and high location precision are essential.

The device offers exceptionally high location accuracy by concurrently connecting to GPS, Galileo and either GLONASS or BeiDou. According to the company, it also provides industry-leading -167 dBm navigation sensitivity, which is said to make it ideal for wearable devices, unmanned aerial vehicles (UAVs) and asset tracker applications.

Thalwil, Switzerland-based u-blox has launched the ZOE-M8G, an ultra-compact GNSS receiver module, especially designed for markets where small size, minimal weight and high location precision are essential.

The device offers exceptionally high location accuracy by concurrently connecting to GPS, Galileo and either GLONASS or BeiDou. According to the company, it also provides industry-leading -167 dBm navigation sensitivity, which is said to make it ideal for wearable devices, unmanned aerial vehicles (UAVs) and asset tracker applications.

The new device helps simplify product designs because it is a fully integrated, complete GNSS solution with built-in SAW-filter and low-noise-amplifier (LNA). This means it can be used with passive antennas, without the need for additional components, and doesn’t compromise performance.

The module measures 4.5mm x 4.5mm x 1mm. Due to its very small size, a complete GNSS design using the module takes approximately 30 percent less printed circuit board (PCB) area compared to a conventional discrete chip design with a CSP chip GNSS receiver.

Uffe Pless, product marketing, positioning product center at u-blox, said: “When you’re designing products such as smart watches, fitness trackers, asset trackers, UBI dongles and even drones, every square millimeter and every gram counts. The u-blox ZOE-M8G makes it significantly easier for product designers to achieve precise location tracking while keeping within their strict form factor and weight restrictions.”

Samples of the u‑blox ZOE‑M8G will be available in February 2017 and volume production will start in October 2017.

Earlier the company said its M8 series of GNSS modules were used in the development of the drone technology for participation in the United Kingdom’s Direct Line Insurance Fleetlights Initiative. The modules provide accurate positioning to a formation of multiple drones that can travel up to 30 mph — while maintaining a distance of approximately two meters between the aircraft, the company said.

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With the Australian government’s announcement earlier this month that it would invest $12 million in a two-year program looking into the future of positioning technology in Australia, comes plans for testing of satellite based augmentation systems (SBAS) to be undertaken, and for future applications for all four major modes of transport in Australia, as well as for potential safety, productivity, efficiency and environmental benefits.

With the Australian government’s announcement earlier this month that it would invest $12 million in a two-year program looking into the future of positioning technology in Australia, comes plans for testing of satellite based augmentation systems (SBAS) to be undertaken, and for future applications for all four major modes of transport in Australia, as well as for potential safety, productivity, efficiency and environmental benefits.

The SBAS test-bed is Australia’s first step toward developing the positioning technology and expertise needed to be competitive globally and could impact the country’s place as an industry leader in the Asia Pacific region. An SBAS would overcome current gaps Australia has in mobile and radio communications and, when combined with on-ground operational infrastructure and services, could ensure that accurate positioning information can be received anytime and anywhere within Australia.

The two-year project will test two new satellite positioning technologies including next generation SBAS and Precise Point Positioning, which will provide positioning accuracies of several decimeters and five centimeters, respectively. Currently, positioning in Australia is usually accurate to five to 10 meters.

“SBAS utilizes space-based and ground-based infrastructure to improve and augment the accuracy, integrity and availability of basic Global Navigation Satellite System (GNSS) signals, such as those currently provided by the USA Global Positioning System (GPS),” stated Federal Minister for Infrastructure and Transport Darren Chester, who added the program could test the potential of SBAS technology in the four transport sectors—aviation, maritime, rail and road.

“The future use of SBAS technology was strongly supported by the aviation industry to assist in high accuracy GPS-dependent aircraft navigation. Positioning data can also be used in a range of other transport applications including maritime navigation, automated train management systems and in the future, driverless and connected cars.”

From using Google Maps on smartphone to emergency management and farming, many people use and benefit from positioning technology every day without even realizing it.

The funding will be used to test instant, accurate and reliable positioning technology that could provide future safety, productivity, efficiency and environmental benefits across many industries in Australia, including transport, agriculture, construction, and resources.
According to the Australian Government, research has shown that the wide-spread adoption of improved positioning technology has the potential to generate upwards of $73 billion of value to Australia by 2030.

The benefits to this improved technology can be widespread. Minister for Resources and Northern Australia Matt Canavan said access to more accurate data about the Australian landscape would also help unlock the potential of the North.

“This technology has potential uses in a range of sectors, including agriculture and mining, which have always played an important role in our economy, and will also be at the heart of future growth in Northern Australia,” Senator Canavan said.
“Access to this type of technology can help industry and Government make informed decisions about future investments.”

The SBAS test-bed is Australia’s first step towards joining countries such as the United States, Russia, India, Japan and many across Europe in investing in SBAS technology and capitalizing on the link between precise positioning, productivity and innovation.

Early this year, Geoscience Australia with the Collaborative Research Centre for Spatial Information (CRCSI) will call for organizations from numerous industries including agriculture, aviation, construction, mining, maritime, rail, road, spatial, and utilities to participate in the test-bed.

GNSS Infrastructure
The SBAS test-bed will utilize existing national GNSS infrastructure developed by AuScope as part of the National Collaborative Research Infrastructure Strategy.

It will test two new satellite positioning technologies — next generation SBAS and Precise Point Positioning, which provide positioning accuracies of several decimeters and five centimeters, respectively.

Highly accurate positioning technologies are already available in Australia, but they can be cost-prohibitive and not readily available in many areas.

Geoscience Australia is working with the CRCSI on the project, which is designed to evaluate the effectiveness of an SBAS for Australia, and build expertise within government and industry on its transformative benefits. The project is funded through the Department of Industry, Innovation and Science, and Department of Infrastructure and Regional Development.

Positioning data has become fundamental to a range of applications and businesses worldwide. It increases productivity, secures safety and propels innovation; enables GPS on smartphones, provides safety-of-life navigation on aircraft, increases water efficiency on farms, helps to locate vessels in distress at sea, and supports intelligent navigation tools and advanced transport management systems that connect cities and regions.

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Swift Navigation co-founders Fergus Noble (29) and Colin Beighley (28) have been honored in the 2017 Forbes 30 Under 30 Consumer Technology list.

Swift Navigation is a San Francisco-based startup that provides centimeter-accurate real-time kinematics (RTK) GPS and GNSS positioning technology for autonomous vehicles, unmanned aerial vehicles (UAVs), precision agriculture, robotics, surveying, space applications and more.

Swift Navigation co-founders Fergus Noble (29) and Colin Beighley (28) have been honored in the 2017 Forbes 30 Under 30 Consumer Technology list.

Swift Navigation is a San Francisco-based startup that provides centimeter-accurate real-time kinematics (RTK) GPS and GNSS positioning technology for autonomous vehicles, unmanned aerial vehicles (UAVs), precision agriculture, robotics, surveying, space applications and more.

The list, which honors 30 young leaders in each of 20 different industries, is "the world’s greatest roll call of young entrepreneurs and game-changers," according to Forbes. Acceptance into its consumer tech list is less than 4 percent.

From self-driving cars and drones to precision agriculture and consumer robotics, Swift Navigation provides centimeter-accurate GPS for a future of autonomous vehicles to navigate the world. With thousands of customers for its first two products, Piksi and Piksi Multi, there has been high demand for its low-cost, high-precision GNSS receivers, according to the company.

"What an honor to be included on the Forbes 30 Under 30 list," said Noble, CTO and co-founder of Swift Navigation. "By developing GPS technology that has evolved from our working together in a barn in Santa Cruz in 2012, we are excited to be helping to shape the way that the world travels, works and innovates in the fields of transportation, surveying, robotics and agriculture, to name just a few of the exciting applications for our products. We’re very grateful to Forbes for acknowledging our work and our commitment to the autonomous future."

Joked co-founder Beighley, "Our only regret is that our third co-founder, Swift CEO Tim Harris, was born a few months too soon to be eligible for the award and aged out at the ripe-old age of 30. We dedicate this award to you, old man!"

For the sixth year in a row, Forbes read through hundreds of applications from young consumer tech innovators to create the 2017 30 Under 30 consumer tech list. Forbes’ editorial staff initially chose a shortlist of about 40 candidates from the applications. These applicants’ names were then sent to a panel of three judges for final selection. The judges this year included notable startup investors Steve Anderson of Baseline Ventures and Aileen Lee of Cowboy Ventures, as well as 30 Under 30 alum and Stripe co-founder John Collison.

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China’s development and promotion of its BeiDou satellite navigation system not only has tremendous implications for that country’s government and finances, but this alternative to GPS also presents a variety of implications for the United States, according to a staff research report released by the U.S.-China Economic and Security Review Commission.

The report, “China’s Alternative to GPS and its Implications for the United States,” was written by Jordan Wilson, policy analyst in security and foreign affairs at the U.S.-China Economic and Security Review Commission, and released on January 5, 2017. Congress established the commission in 2000 “to monitor, investigate, and submit to Congress an annual report on the national security implications of the bilateral trade and economic relationship between the United States and the People’s Republic of China, and to provide recommendations, where appropriate, to Congress for legislative and administrative action.” The Commission is composed of 12 members who serve two-year terms, three of whom are selected by each of the majority and minority leaders of the Senate, and the speaker and the minority leader of the House of Representatives.

Although BeiDou is provided for free and therefore is not designed to “compete” with other satellite navigation systems, including GPS, the commission noted that Beijing has implemented a number of domestic policies to promote the adoption of BeiDou-compatible receivers and expand its GNSS industry.

Plenty of reasons exist to believe this expansion will be substantial, Wilson writes. Not only are expectations high for significant economic growth brought on by BeiDou, but the development of one of the world’s first satellite navigation systems will also affect diplomatic issues and should also play a big role in providing China with both domestic and international prestige.

The Commission’s report states that, according to Davof Xu, GNSS China project manager for the European Union Chamber of Commerce in China, more than 14,000 companies and organizations are active in the GNSS-related industry in China, accounting for a total of more than 450,000 employees. Xu’s comments came from a commission staff interview last September.

The industry’s fast-paced growth along with China’s “relatively low current market share, latecomer status, and fragmented market likely indicates to Beijing an opportunity for significant economic benefits down the road,” the report states. An earlier report prepared for the U.S.-China Economic and Security Review Commission, “Planning for Innovation: Understanding China’s Plans for Technological, Energy, Industrial, and Defense Development,” by the University of California Institute on Global Conflict and Cooperation (IGCC) on July 28, 2016, said that Chinese GNSS companies are mostly small- and medium-sized in comparison to highly consolidated global providers.

In the commission’s January 5 report, commercial implications for both the U.S. and China are addressed. Wilson writes that the global trend toward “product compatibility with multiple constellations may actually create additional opportunities for U.S. companies in China in the near term.” He cites both the IGCC report and Qualcomm Inc.’s press releases to support these findings. Qualcomm that provided the GNSS component technology for the first GPS- and BeiDou-compatible smartphone — the China market version of the Samsung Galaxy Note 3 — in 2013.

China’s public release of BeiDou’s Open Service interface control document (ICD) as other GNSSs have done indicates a desire for some level of international participation. (The most recent version, BeiDou ICD v2.1, was released last November.) An official with the China Satellite Navigation Office noted in 2014 that “Chinese companies can only grow by engaging in face-to-face competition with the world’s top companies in the industry.”

The report notes, however, that it takes roughly one to two years to add a new constellation to product software, which can be a disadvantage for foreign companies that have not yet developed BeiDou compatibility. Additionally, Wilson said that China’s government has taken steps to advantage domestic companies, which most other GNSS programs have done at some point in their development. These steps include:
• Chinese companies were given early access to BeiDou reference designs and specifications, providing an early advantage in developing BeiDou products.
• China has established certification hurdles seemingly targeting non-Chinese vendors.
• According to the report, Qualcomm was not allowed to join China’s GNSS standards committee alongside domestic firms.

Because of these factors, U.S. and other foreign firms will likely need some time to take advantage of the commercial shift toward multi-constellation devices to compete in China’s domestic market.

In the international market, U.S. companies will likely be able to configure their products to work with the constellations prevalent in each region and compete with fewer obstacles than in China’s domestic market, the report argued. From this perspective, rapid movement towards full interoperability between satellite navigation systems will continue to be in the interests of the United States, the report indicates.

Challenges specific to the downstream satellite navigation industry in China should be included in larger discussions regarding U.S.-China trade and market access issues. As Mr. Xu observes, market access for foreign firms in China is a “general not a particular problem.”

More on BeiDou
China’s Beidou satellite navigation system is projected to achieve global coverage by 2020, providing position accuracies better than 10 meters (one meter or less with regional augmentation) using a network of 35 satellites. China has sought to field its own satellite navigation system for a variety of reasons.

According to the executive summary in Wilson’s report, China’s reasons include to: (1) address national security requirements by ending military reliance on GPS; (2) build a commercial downstream satellite navigation industry to take advantage of the quickly expanding market; and (3) achieve domestic and international prestige by fielding one of only four such global navigation satellite systems (GNSS) yet developed, cementing China’s status as a leading space power and opening the door to international cooperation opportunities. BeiDou is consistently referenced as one of China’s top space projects in its government white papers on space activities, most recently in December 2016.

Without a doubt, Wilson asserted, BeiDou is of foremost importance in allowing China’s military to employ Beidou-guided conventional strike weapons if access to GPS were denied. In addition to the open service, BeiDou transmits an encrypted signal intended for military or security use, as most other GNSSs also do.

In terms of specific security issues, the report notes that “concerns have been raised regarding inherent security vulnerabilities in BeiDou-equipped receivers.” These included the possibility of tracking users without their knowledge or introducing “malware” into their products, suggestions that Wilson indicated were raised by the Ministry of Science and Technology of Taiwan, with which mainland China has a problematical relationship. Technical experts interviewed for the report, however, noted that (1) they are not aware of ways to feasibly transmit malware through a navigation signal; and (2) receiver chip manufacturers outside China will be unlikely to include the BeiDou “texting service” due to cost factors.

Jim Mollenkopf, senior director of strategic development for Qualcomm Government Technologies, stated that “Qualcomm’s products only use BeiDou for passive reception of navigation signals, and do not use the BeiDou messaging function. We know of no way for the BeiDou system to track users without the messaging function enabled.”

Lastly, Wilson states that given that one-third of the smartphones exported from China in the first quarter of 2016 reportedly contained BeiDou receiver chips, U.S. consumers should know that there are no inherent risks involved in a smartphone having BeiDou connectivity if it does not include the satellite communication channel. The report cites Stephen Chen, “China to Massively Increase Accuracy of its GPS Rival, with Benefits for Smartphone Users … and the Military,” from the South China Morning Post (June 17, 2016) with providing these figures.

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Securities regulators recently approved a plan to improve the tracking of financial trades by creating a single, comprehensive database that, among other things, incorporates tightened clock synchronization standards.

The consolidated audit trail, or CAT, lays out how customer and deal information will be recorded, reported and maintained throughout the complete lifecycle of all orders and transactions in the U.S. equity and options markets. The data would have to be recorded contemporaneously with the order events and reported to a central repository by 8:00 a.m. Eastern Time on the day following the event. The 340-page plan, which was published in the Federal Register on November 23, also lays out a set of clock synchronization standards and a plan for determining updates to those standards that industry will have to implement.

"What the SEC is saying is that timing is becoming more and more critical in the financial industry," said Andrew Bach, a financial timing expert and a member of the Advisory Board of the Resilient Navigation and Timing Foundation.

The markets generally have been moving faster and faster for the last 300 years, said Bach, and "it’s really gotten exciting in the last few decades." With Twitter feeds and instantaneous news driving microsecond decisions it is hard for financial regulators to keep the markets fair.

"The SEC is in a position of saying: ‘How do I know if someone inappropriately traded ahead of the market because they knew something that no one else did, when the difference between trading ahead of the market and the general dissemination of news is now measured in a fraction of a second?" Bach explained.

In determining the standards, the SEC distinguished between different operators in the market, demanding more of stock exchanges and other venues like alternative trading systems (ASTs) and electronic communication networks, also called ECNs. ECNs are automated systems that match buy and sell orders for securities so that major brokerages and individual traders can ditch the middlemen and trade directly between themselves.

These types of operations venues will be required to synchronize the business clocks for their electronic systems to, at a minimum, within 100 microseconds of the time maintained by the National Institute of Standards and Technology (NIST), consistent with industry standards.

Broker-dealers, that is traders like Merrill Lynch or E*Trade, must synchronize their electronic systems’ clocks to within 50 milliseconds of NIST time.

"The exchange is held to a tighter standard," said Bach.

Overall the timing requirements will enable regulators to better sequence the placement of, and action on, orders across multiple exchanges, the SEC plan decision said.

"The only way for them to regulate effectively is to know when events really happen with a high degree of precision," said Bach. "And whatever that precision is, it’s going to be even more precise next year and the year after — and the year after that."

In fact, for timing professionals the most interesting part may be what comes next. The SEC is requiring annual reviews of clock technology to "ensure that clock synchronization standards remain as tight as practicable in light of technological developments."

As part of those reviews the SEC wants the industry to determine, and report back on, whether tougher standards are in order.

The speed of information and increasing number of information channels forces the SEC and other regulators to determine some sort of sequence of events, explained Bach, "to figure out — did someone trade ahead? Did someone trade behind?

"That drives them," said Bach, "to write tighter and tighter numbers. Whatever number you find in the documents I think the one thing we can be confident of — it’s going to be a much lower number a year or two ahead from now."

Previously, Inside GNSS had reported on how financial networks were shifting to GPS-Stamped Precise Time.

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