A French vineyard in the Loire Valley is a beautiful sight, but the farm is struggling to recruit workers to weed its organically grown grapes. The younger generation has moved to the cities, leaving urban communities short of manpower. Solution: a fully autonomous tractor, developed by a French company based in Sitia, working tirelessly to weed the narrow rows between the vines. After a full month of weeding and hundreds of kilometers covered, Sitia approached the farmer to take stock of the work done, and reimburse 2 vines, which were damaged during the month of operation. They were surprised to hear the farmer exclaim: “When I use my manual tractor to do the same job, I spoil at least two vines a day! How did your robot manage to be so careful?”. A high level of precision and integrity guarantees a high quality of autonomous operation.
But what exactly is positioning integrity and how does it affect the performance and reliability of robots and other autonomous machines? Integrity is the truthfulness of positioning and positioning accuracy information, even if it means showing that the current position information is not as accurate as desired in a certain harsh environment. Part of providing high integrity positioning is statistical analysis called RAIM or RAIM+, where the latter takes that analysis to the next level as part of a larger positioning protection package.
Why is integrity essential for automation?
Let’s take a closer look at GNSS receiver integrity in the sense of truthful reporting of possible positioning inaccuracies, and how overly optimistic reporting can lead to unsafe autonomous operation. Communicating receiver accuracy is through positioning uncertainty, which is the maximum possible error in the calculated position. This gives an idea of the risk of positioning error, particularly necessary in difficult GNSS environments where the receiver only “sees” a limited number of GNSS satellites or when GNSS signals are degraded. Such error reporting is important for all standalone machines, but especially for guaranteed PNT applications and critical operations. Keep in mind that a consistent position may seem correct but may still be incorrect. Positioning uncertainty indicates how much positioning accuracy you can rely on at any given time.
The blue line in the diagram below shows the position uncertainty estimated by a GNSS receiver under favorable conditions, when the view of the sky is unobstructed and the receiver has a direct line of sight to many satellites. The receiver operator can set an alarm limit (shown in red), so that the receiver can signal situations where the positioning uncertainty becomes too great.
Under favorable conditions, the positioning uncertainty remains well below the alarm limit because the calculated position is almost the same as the actual position of the robot. However, in harsh environments, the accuracy of positioning uncertainty becomes more critical (see Figure 2). For example, when the view of the sky is partially obstructed by buildings or foliage, the receiver only has access to a limited number of GNSS satellites, which makes it more difficult to calculate an accurate position. In such cases, the receiver should signal a higher positioning uncertainty, so that the system can take adequate action such as shifting to lower speeds, staying further away from pre-set limits, or stopping.
A receiver with low integrity may continue to report optimistic positioning uncertainty, which remains below the preset alarm limit even when the calculated position is very far from the actual position. The number may seem correct, but it effectively becomes a “robot on the loose”, which is no longer on its intended path with a risk of harming itself and its surroundings.
Let’s look at uncertainty bounds in action during a GNSS car test in an urban canyon, where the view of the sky is partially obstructed by houses. The orange lines are the positioning and its uncertainty bounds reported by the tiled GNSS module in the car, while the red lines are the positioning and its uncertainty bounds reported by another popular GNSS receiver. The white line indicates the actual position of the car when driving on the road. The mosaic receiver’s orange uncertainty bounds are true and a bit wider in this harsh environment, and you can see that the true position always stays within those bounds. On the other hand, the red trajectory deviates from its trajectory at a certain difficult point of the route, the real position being no longer within the limits of uncertainty, which remain too optimistic. In this case, the competitor’s receiver gives a false sense of security and the system is unaware of its dangerous operation.
If the receiver represented by the red line was providing navigation information for an ADAS automotive system, for example, this could mislead the system into thinking the car has changed lanes. If the system then tried to correct the trajectory by returning to the “right lane”, it would have the consequence of causing the car to deviate from its trajectory and potentially hit the curb or even another car.
RAIM vs. RAIM+
The underlying mechanism behind the truthful positioning uncertainty report is RAIM (Receiver Autonomous Integrity Monitoring), which ensures a truthful positioning calculation based on statistical analysis and the exclusion of any outlier satellites or signals. Septentrio receivers are designed for high integrity and take RAIM to the next level with RAIM+, ensuring positioning accuracy with a high degree of confidence. In Septentrio receivers, RAIM+ is actually a component of a complete receiver protection suite called GNSS+ including positioning protection at different levels including AIM+ anti-jamming and anti-spoofing, resiliency IONO+ to ionospheric scintillations and APME+ multipath attenuation.
The Septentrio RAIM+ statistical model has been refined with more than 50 terabytes of field data collected over 20 years. It suppresses satellites and signals susceptible to errors due to multipath reflection, solar ionospheric activity, jamming and spoofing while working with the GNSS+ components mentioned above. Thanks to this multi-component protection architecture, it achieves a very high level of positioning accuracy and reliability that goes far beyond standard RAIM. The RAIM+ statistical model is adaptive, highly detailed and comprehensive, taking advantage of all available GNSS constellations and signals. Full RAIM+ functionality is even available in Septentrio’s range of GNSS/INS receivers. User-controlled settings allow it to be tailored to specific requirements.
The diagram below shows RAIM+ in action during a jamming and spoofing attack on a Septentrio GNSS receiver. While AIM+ removes the effects of GNSS jamming, AIM+ and RAIM+ work together to block the spoofing attack. Satellites with high distance errors, shown in the middle graph, are suppressed by RAIM+ because they do not conform to the expected satellite distance.
Figure 5: In this scenario, the jamming gives satellite distance errors but is countered by AIM+ technology. During spoofing, AIM+ drops some of the spoofed satellites, while other satellites that have the wrong ranges are discarded by the RAIM+ algorithms.
This example shows that even in the face of jamming and spoofing, Septentrio’s high integrity receiver technology provides truthful and reliable positioning that any autonomous system can rely on.
GNSS design around reliability
GNSS receivers designed to be reliable strive for high integrity in both positioning uncertainty reporting as well as advanced RAIM+ statistical modeling. This ensures that these receivers provide truthful and timely warning messages and are resilient in various harsh environments. Other technologies such as INS (Inertial Navigation System) can also be coupled with the GNSS receiver to extend positioning availability even during short GNSS outages. Quality indicators for satellite signals, processor status, base station quality and overall quality allow monitoring of positioning reliability at all times. High integrity GNSS receivers provide accurate positioning in autonomous machines such as the Sitia weeding tractor. They are also crucial components in safety critical applications, assured PNTs and any other application where accuracy and reliability are important.