The Inertialist: Up close with inertial navigation – Inside GNSS

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Inside GNSS’ resident inertial expert examines coasting on GNSS failures, navigating in harsh environments, and mitigating interference in a sensor fusion environment.

As positioning experts search for ways to close GNSS vulnerability gaps, many alternative navigation sources and sensors are emerging, each with their own limitations. No single technology provides accurate and reliable positioning, navigation and timing anywhere and anytime. Instead, we need to combine the complementary benefits of different navigational aids through sensor fusion.

How do you build a sensor fusion system? Almost all navigational aids draw information from some external source which is not always available. A notable exception provides a completely self-contained solution. This is inertial navigation. An inertial navigation system (INS) measures non-gravitational accelerations and angular rates and incorporates them into navigation outputs. The underlying physical effects are still there and the system can operate in any environment at any time.

As a result, robust and reliable anywhere, anytime positioning can be enabled by INS-centric sensor fusion that seamlessly links various navigational aids around the inertia. In this case, the INS serves as the central sensor to provide navigation outputs while other (environment dependent) navigation sensors provide aiding data to reduce drift in the inertial navigation outputs. Such a complementary merger allows

• seamless addition of help data, when available;

• continuity of the solution in various environments;

• and robust and resilient state estimation in contested environments where corrupted measurements (eg, GNSS multipath with no line-of-sight) are mitigated using INS-based filtering.

Recent advances in inertial navigation technology and, in particular, micro-electromechanical inertial measurement units (IMU MEMS), make it possible to find a suitable IMU option for a wide range of applications, from power level 2 to $3 at a tactical level. to the quality of navigation. As a result, inertial navigation is already at the heart of many existing systems and its use will continue to grow in the future.

In the first issue of this column, we take a closer look at the claim that there is an INS option for every budget.

Sure, there are relatively expensive IMUs out there that can be used, but are there any inertial sensor navigation benefits that only cost a few dollars?

Benefits of Consumer MEMS

Fortunately, the performance of consumer MEMS sensors has continued to improve. To illustrate, Figure 1 shows the stability of an industrial-grade MEMS gyroscope.

The activation drift value can still be quite large. However, this drift is stable and can be effectively calibrated during the initialization phase, when the system is at rest. The residual stochastic variations remain at a level of 20 degrees/h, which is sufficient to provide substantial advantages from a navigation point of view. These include:

• cabotage on GNSS failures;

• Reliable navigation in challenging GNSS environments such as dense urban canyons; and,

• Interference mitigation, including interference suppression and protection against spoofing attacks.

Figure 2 illustrates these advantages for ground-vehicle applications, showing a high-level architecture of an example of an INS-centric sensor fusion system.

This system combines inertial measurements with GNSS measurements (pseudoranges, Doppler, carrier phase) and ground-vehicle motion constraints (including zero velocity updates, as well as zero lateral and vertical velocity constraints). The integrating filter fuses the INS with auxiliary sources using a complementary concept of sensor fusion. Instead of estimating navigation states themselves, it estimates errors in navigation states.

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This process

• enables efficient linearization of system relationships such that the integrating filter can be implemented as a computationally efficient extended Kalman filter (EKF);

• and greatly simplifies dynamic state modeling since slowly changing INS errors can be modeled much more reliably compared to fast changing navigation states.

EKF measurement observations are formulated as differences between actual measurements and their INS-based predictions. Also, comparing INS predictions with actual measurements provides a very effective tool for detecting and removing outliers in aid data (e.g. GNSS multipath or unreliable speed constraints when turning tight).

In addition to merging inertial sources with measurement-level assisting sources (commonly referred to as tight coupling), the sensor fusion architecture in Figure 2 also supports data fusion at signal processing or deep coupling level. Deep coupling enables extremely long coherent integration (LCI) of received GNSS signals: 1 second and beyond compared to 20 ms in a traditional receiver architecture. This

• Reduces signal processing bandwidth, eliminating spoofing, and

• Increases received signal strength while eliminating noise and interference.

LCI is made possible and practical with the help of accurate GNSS signal accumulation from a consumer grade INS which in turn benefits from sustained GNSS updating.

Coasting over GNSS failures

picture 3 shows an example of coasting performance. In this case, a tightly coupled implementation (without the deep integration component) is used. The experimental results here were produced with an off-the-shelf GNSS receiver chipset (u-blox M8T), ST Micro MEMS IMU (ISM330DHCX), and QuNav’s embedded GNSS/Inertial Vehicular Engine (GIVE) software.

Figure 3(a) shows trajectory estimates for a tunnel test with two full failures that lasted about 30-40 seconds. Figure 3(b) shows a significantly longer downtime event, where the vehicle was driven in an underground parking lot for more than 5 minutes. The position outputs of the GNSS/INS integration and GNSS/INS/motion constraint options are displayed.

Relatively short GNSS outages (about 30 seconds) can be reliably bridged with INS alone. However, for longer interrupt durations, performance begins to degrade, as shown in Figure 3(b). Downtime can be extended substantially if INS help sources are further increased with movement constraints. In this case, the system can withstand a long GNSS unavailability (5 minutes) while limiting position errors to a level of about 10 meters.

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Environments challenged by GNSS

Figure 4 shows examples of test results in dense urban environments (downtown San Francisco). Again, a tightly coupled sensor fusion option was used with u-blox GNSS, ST Micro IMU and QuNav sensor fusion. Position output from Novatel SPAN (using a tactical-grade IMU and generated in post-processed mode) was used for verification.

Test results demonstrate that the constrained motion INS integration option limits position errors to 4 meters (with 50% error limit) and 10m (with 99% error probability) in very harsh GNSS environments in dense urban canyons subject to severe multipath.

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Interference mitigation

Deep integration of INS-centric sensor fusion with GNSS helps mitigate jamming signals and suppress spoofing attacks in a small form factor system that does not need to use multi-element antenna arrays. As mentioned earlier, deep integration applies an inertial aid to greatly increase the accumulation interval of received GNSS signals. For example, an LCI of 1 second can be maintained.

The use of LCI is extremely beneficial in mitigating spoofing attacks, thereby protecting GNSS signals in open service. Specifically, the signal processing bandwidth is reduced to 1 Hz or less. It is therefore extremely difficult to launch a successful attack because the spoofer must be able to

• tracking receiver movement and clock states to sub-Hz level (or equivalently, sub-decimeter-per-second precision);

• and align its signal with the authentic one at the same level of precision.

Figure 5(a) illustrates identity theft mitigation capability. In this case, smart spoofing was software injected into pre-recorded baseband signal samples of the GPS Link 1 C/A signal, which were then post-processed by the Software Defined Receiver (SDR) of QuNav deeply integrated with mainstream MEMS IMU. (which was used for the GNSS outages and dense urban tests discussed above).

The spoofer implemented a positional push and successfully hijacked the signal processing functionality of an unaided receiver. In contrast, the deeply integrated system maintained lock on the authentic signal throughout testing, making the system impervious to spoofing.

LCI also provides an additional 20 dB of interference protection, including the most difficult case of matched-spectrum (broadband) interference. To illustrate, Figure 5(b) shows test results for a ground vehicle test scenario where a 40 dB signal attenuation was introduced by wideband jamming software injection into the GLONASS L1 signal data. This attenuation level is 20 dB below an unassisted receiver tracking threshold.

Sub-metric positioning capabilities have been maintained using a MEMS IMU chipset. In addition to interference suppression, prolonged signal accumulation is also beneficial for weak signal recovery. Specifically, the 20dB anti-jamming protection equates to a 20dB gain in weak GNSS signal processing, which is directly beneficial for degraded environments such as dense urban areas and under multiple canopy layers.

Conclusion

In summary, consumer MEMS inertial sensors offer substantial benefits for various environments degraded by GNSS. As the test results illustrate, the tightly coupled GNSS/INS supports position continuity over relatively short GNSS outages (30-40 seconds). This duration of unavailability can be increased (up to 5 minutes or more) if the mechanization of the system is supplemented by other aids to navigation (such as movement constraints for land vehicles). Additionally, a tightly coupled solution enables reliable localization in contested GNSS environments such as urban canyons. Finally, the extension of tight coupling to deep integration helps to mitigate interference (jamming and spoofing).

In the next issue, we’ll look at the fundamentals and discuss the main principles of inertial navigation.

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