Navigator Notes

VIDEO ABSTRACTS

Video Abstracts allow authors to present their research in their own words. This multimedia format communicates the background and context of authors’ research in a quick and easy way, elevating research from simple print delivery.

Video for “A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems”

By Nader Al Bitar and Alexander Gavrilov (https://www.ion.org/publications/abstract.cfm?articleID=102922)

Abstract: Aiming to improve the position and velocity precision of the INS/GNSS system during GNSS outages, a novel system that combines unscented Kalman filter (UKF) and nonlinear autoregressive neural networks with external inputs (NARX) is proposed. The NARX-based module is utilized to predict the measurement updates of UKF during GNSS outages. A new offline approach for selecting the optimal inputs of NARX networks is suggested and tested. This approach is based on mutual information (MI) theory for identifying the inputs that influence each of the outputs (the measurement updates of UKF) and lag-space estimation (LSE) for investigating the dependency of these outputs on the past values of the inputs and the outputs. The performance of the proposed system is verified experimentally using a real dataset. The comparison results indicate that the NARX-aided UKF outperforms other methods that use different input configurations for neural networks.

Article Citation: Al Bitar, N, Gavrilov, A. A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems. NAVIGATION. 2021; 68(3): 521–539. https://doi.org/10.1002/navi.435

Video for “Sensitivity of advanced RAIM performance to mischaracterizations in integrity support message values”

By Young Lee, Brian Bian, Ali Odeh, and Jianming She (https://www.ion.org/publications/abstract.cfm?articleID=102923)

Abstract: With the increasing number of navigation satellite constellations from multiple service providers, an emerging concept for receiver avionics is being developed which will significantly improve the current, widely available operations based on the classical Receiver Autonomous Integrity Monitoring (RAIM). This emerging concept, called Advanced RAIM (ARAIM), is envisioned to eventually provide a worldwide approach capability for Localizer Performance with Vertical Guidance (LPV). One major architectural change of ARAIM from RAIM is introduction of the Integrity Support Message (ISM), which is a set of parameters representing the statistical characterization of the core navigation constellation performance. The ISM values will be periodically updated and used as a priori information in the ARAIM user algorithm. Therefore, it is critical to broadcast ISM values that would provide optimal ARAIM performance balanced between the margins of integrity, continuity, and availability. In this paper, we analyze the sensitivity of ARAIM performance to potential ISM mischaracterization.

Article Citation: Lee, Y, Bian, B, Odeh, A, She, J. Sensitivity of advanced RAIM performance to mischaracterizations in integrity support message values. NAV-IGATION. 2021; 68(3): 541–558. https://doi.org/10.1002/navi.437

Video for “Air data fault detection and isolation for small UAS using integrity monitoring framework”

By Kerry Sun and Demoz Gebre-Egziabher (https://www.ion.org/publications/abstract.cfm?articleID=102925)

Abstract: A Fault Detection and Isolation (FDI) algorithm is developed to protect against Water-Blockage (WB) pitot tube failure in the safety-critical Air Data System (ADS) used on small Unmanned Aircraft Systems (UAS). The algorithm utilizes two identical Synthetic Air Data Systems (SADS) as the basis for state estimation. Each SADS works independently with a pitot tube while sharing an IMU and GNSS receiver. The fault detection is designed using the integrity monitoring framework, and the isolation is obtained via independent fault detection channels. The ADS requirements are established, and the WB failure mode is analyzed based on real faulty air data. A new residual-based test statistic is introduced, and the link among the test statistic, observability matrix, and Minimal Detectable Error (MDE) are examined. Finally, a flight data set with a known water-blockage fault signature is used to assess the algorithm’s performance in terms of the air data protection levels and alert limits.

Article Citation: Sun, K, Gebre-Egziabher, D. Air data fault detection and isolation for small UAS using integrity monitoring framework. NAVIGATION. 2021; 68(3): 577– 600. https://doi.org/10.1002/navi.440

Video for “Data-driven protection levels for camera and 3D map-based safe urban localization”

By Shubh Gupta and Grace Gao (https://www.ion.org/publications/abstract.cfm?articleID=102928)

Abstract: Reliably assessing the error in an estimated vehicle position is integral for ensuring the vehicle’s safety in urban environments. Many existing approaches use GNSS measurements to characterize protection levels (PLs) as probabilistic upper bounds on position error. However, GNSS signals might be reflected or blocked in urban environments, and thus additional sensor modalities need to be considered to determine PLs. In this paper, we propose an approach for computing PLs by matching camera image measurements to a LiDAR-based 3D map of the environment. We specify a Gaussian mixture model probability distribution of position error using deep neural-network-based data-driven models and statistical outlier weighting techniques. From the probability distribution, we compute PL by evaluating the position error bound using numerical line-search methods. Through experimental validation with real-world data, we demonstrate that the PLs computed from our method are reliable bounds on the position error in urban environments.

Article Citation: Gupta, S, Gao, G. Data-driven protection levels for camera and 3D map-based safe urban localization. NAVIGATION. 2021; 68(3): 643–660. https://doi.org/10.1002/navi.445

WEBINARS

ION Webinars highlight timely and engaging articles published in NAVIGATION and other topics of interest to the PNT community in an interactive virtual presentation.

November 18, 2021 Webinar: Data-driven protection levels for camera and 3D map-based safe urban localization

By Shubh Gupta (https://www.ion.org/publications/abstract.cfm?articleID=102928)

Abstract: Reliably assessing the error in an estimated vehicle position is integral for ensuring the vehicle’s safety in urban environments. Many existing approaches use GNSS measurements to characterize protection levels (PLs) as probabilistic upper bounds on position error. However, GNSS signals might be reflected or blocked in urban environments, and thus additional sensor modalities need to be considered to determine PLs. In this paper, we propose an approach for computing PLs by matching camera image measurements to a LiDAR-based 3D map of the environment. We specify a Gaussian mixture model probability distribution of position error using deep neural-network-based data-driven models and statistical outlier weighting techniques. From the probability distribution, we compute PL by evaluating the position error bound using numerical line-search methods. Through experimental validation with real-world data, we demonstrate that the PLs computed from our method are reliable bounds on the position error in urban environments.

Article Citation: Gupta, S, Gao, G. Data-driven protection levels for camera and 3D map-based safe urban localization. NAVIGATION. 2021; 68: 643660. https://doi.org/10.1002/navi.445

October 28, 2021 Webinar: Air data fault detection and isolation for small UAS using integrity monitoring framework

By Kerry Sun (https://www.ion.org/publications/webinar-sun.cfm)

Abstract: A Fault Detection and Isolation (FDI) algorithm is developed to protect against Water-Blockage (WB) pitot tube failure in the safety-critical Air Data System (ADS) used on small Unmanned Aircraft Systems (UAS). The algorithm utilizes two identical Synthetic Air Data Systems (SADS) as the basis for state estimation. Each SADS works independently with a pitot tube while sharing an IMU and GNSS receiver. The fault detection is designed using the integrity monitoring framework, and the isolation is obtained via independent fault detection channels. The ADS requirements are established, and the WB failure mode is analyzed based on real faulty air data. A new residual-based test statistic is introduced, and the link among the test statistic, observability matrix, and Minimal Detectable Error (MDE) are examined. Finally, a flight data set with a known water-blockage fault signature is used to assess the algorithm’s performance in terms of the air data protection levels and alert limits.

Article Citation: Sun, K, Gebre-Egziabher, D. Air data fault detection and isolation for small UAS using integrity monitoring framework. NAVIGATION. 2021; 68(3): 577– 600. https://doi.org/10.1002/navi.440

October 18, 2021 Webinar: How insect brains perform dead reckoning

By Barbara Webb (https://www.ion.org/publications/webinar-webb.cfm)

Background: Insects such as bees and ants are known to use dead reckoning to return in a ‘bee-line’ to their nest after excursions of more than a kilometer. Recent anatomical and neurophysiological investigations have now established the underlying brain circuits that enable this behavior. These can be shown to carry out the required geometric operations when replicated as detailed computational models and tested on robots.

September 14, 2021 Webinar: Performance assessment of GNSS diffraction models in urban areas

By Guohao Zhang and Li-Ta Hsu (https://www.ion.org/publications/webinar-zhang.cfm)

Abstract: The GNSS performance is significantly degraded in urban canyons because of the signal interferences caused by buildings. Besides the multipath and nonline-of-sight (NLOS) receptions, the diffraction effect frequently occurs in urban canyons, which will severely attenuate the signal strength when the satellite line-of-sight (LOS) transmitting path is close to the building edge. It is essential to evaluate the performance of current diffraction models for GNSS before applying mitigation. The detailed steps of applying the knife-edge model and the uniform geometrical theory of diffraction (UTD) model on GNSS are given, including the C/N₀ and pseudorange simulation of the diffracted signal. The performances of both models are assessed using real data from two typical urban scenarios. The result shows the UTD can adequately model the GNSS diffraction effect even in a complicated urban area. Compared with the knife-edge model, the UTD achieves better modeling accuracy, whereas it requires higher computational loads.

Article Citation: Zhang, G, Hsu, L-T. Performance assessment of GNSS diffraction models in urban areas. NAVIGATION. 2021; 68(2): 369–389. https://doi.org/10.1002/navi.417