Mu Jia and Zak (Zaher) Kassas, The Ohio State University

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Passenger safety in ground vehicles depend on the accuracy and reliability of the vehicle’s navigation system. This is particularly the case for semi- and fully-automated vehicles. With the navigation accuracy keeping improving, the concept of integrity also attracts more and more attention from urban users. This is because positioning using GNSS signals can be unreliable in deep urban canyons, due to the blockage, reflection or diffraction of the GNSS signals by the buildings. Recently, fusing signals of opportunity (SOPs) in GPS navigation systems has been proven to improve the accuracy and integrity of navigation solutions, when GPS signals become unavailable or degraded. While terrestrial SOPs provide more redundancy and geometric diversity for navigation systems, the poor signal reception conditions in urban environments are still overwhelmingly challenging for high-integrity navigation. To improve the availability of safe vehicular navigation, this paper proposes a novel Kalman filter-based receiver autonomous integrity monitoring (RAIM) algorithm which fuses GNSS, cellular SOPs and inertial sensors to exploit sequential and multi-sensor measurements for extended redundancy. 3D maps are also utilized to deal with environmentdependent threats. Ground vehicle navigation systems utilize global navigation satellite systems (GNSS) receivers and a suite of onboard sensors, e.g., lidar, camera, radar, inertial navigation system (INS), etc. GNSS are relied upon to provide a navigation solution in a global frame and to correct for accumulating errors due to sensor dead reckoning. While achieving higher levels of navigation accuracy has been a classic requirement, the trustworthiness in the navigation solution, commonly assessed by integrity measures, is evermore vital in the safety critical application of automated driving. To ensure safe navigation, automated vehicles need to tightly bound the navigation errors and ensure that the probability of navigation errors being not properly bounded is below a certain limit. Current GNSS technologies are insufficient to support the transition of ground vehicles to full automation in terms of accuracy, integrity, and availability [1]. In terms of accuracy, sub-meter-level accuracy is achievable with certain augmentation systems and real-time kinematic (RTK) only under certain favorable conditions [2]; while single point positioning (SPP) can only achieve meter-level accuracy [3]. In terms of integrity and availability, recent work demonstrated that in a sample downtown environment (Chicago urban corridor), availability of GPS-only positioning was less than 10% at most locations. While using multi-constellation GNSS (GPS, GLONASS, Galileo, and Beidou) improved the availability significantly, it was still lower than 80% at certain points; concluding that multi-constellation GNSS cannot provide continuous vehicle positioning along the street [4]. Recently, signals of opportunity (SOPs), e.g., cellular signals [5] and digital television signals [6], have been been demonstrated as an attractive alternative or supplement to GNSS signals. SOPs could provide a navigation solution in a global frame in a standalone fashion [7, 8] or aid dead reckoning sensors (e.g., INS [9]). For vehicular navigation in urban environments, cellular SOPs are particularly attractive due to their inherent attributes: abundance, geometric and spectral diversity, high received power, and large bandwidth. When used alongside GNSS signals, SOPs could improve the accuracy, integrity, and availability of the navigation system. GNSS-based integrity monitoring has been studied extensively [10]. Among the proposed frameworks, receiver autonomous integrity monitoring (RAIM) is exceptionally attractive, as it is cost-effective and does not require building additional infrastructure [11]. RAIM has been adapted to account for multi-constellation GNSS measurements [12] (e.g. Galileo [13], GLONASS [14], and Beidou [15]), aiding sensors (e.g., INS-GPS [16], lidar-GNSS [17], and vision-GPS [18]), and terrestrial SOPs [19, 20]. Initial studies to characterize the integrity monitoring improvement for automated driving, upon fusing GPS signals with terrestrial SOPs, was conducted in [21, 22]. However, this study assumed fault-free measurements, which is not realistic in urban environments, in which multipath effects and non-line-of-sight (NLOS) conditions are prevalent. [23, 34] characterized the influence of multipath and NLOS effects on the integrity performance and demonstrated improved availability in deep environments by fusing cellular SOPs. Nevertheless, the availability rates are still far from full coverage of assured navigation. Up until recently, most of the integrity monitoring frameworks have relied on snapshot RAIM, i.e., RAIM based on static (e.g., weighted least square) estimators, due to their straightforward projection of measurement error distribution on the solution domain. However, frequent and severe multipath effects can easily make snapshot RAIM fail. This is because RAIM is built on the assumption that nearly the full set of the measurements from each time step can form a consistent set. Otherwise, snapshot RAIM is likely to fail locating the unfaulted subset of measurements. Furthermore, multipath and NLOS errors are environment dependent. It is difficult to model the multipath and NLOS errors as a deterministic distribution, which is a necessary prior for the threats to be monitored by RAIM. To tackle this problems, this paper develops a novel Kalman filter-based RAIM framework to fuse sequential measurements from GNSS, cellular SOPs and an INS. Furthermore, it exploits 3D maps to pre-filter measurement outliers. Solution separation tests are further conducted to monitor and exclude multiple faults. Three contributions will be made in this paper. First, a Kalman filter-based RAIM is proposed to incorporate multi-constellation GNSS, SOPs, and inertial sensors. Second, this paper will study the 3D map-based outlier rejection algorithm which is developed based on ray-tracing, and compare it with innovation-based outlier rejection. Third, field experiments will be conducted in multiple urban scenarios to test the performance of the proposed framework. REFERENCES [1] N. Zhu, D. Betaille, J. Marais, and M. 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Kassas, “Evaluation of ground vehicle protection level reduction due to fusing GPS with faulty terrestrial signals of opportunity,” in Proceedings of ION International Technical Meeting Conference, 2021, pp. 354–365. [23] M. Jia, H. Lee, J. Khalife, Z. Kassas, and J. Seo, “Ground vehicle navigation integrity monitoring for multi-constellation GNSS fused with cellular signals of opportunity,” in Proceedings of IEEE International Conference on Intelligent Transportation Systems, September 2021, pp. 3978–3983. [24] "M. Jia and Z. Kassas," Kalman filter-based integrity monitoring for GNSS and 5G signals of opportunity integrated navigation, IFAC Symposium on Advances in Automotive Control, Aug. 29-31, 2022, Columbus, OH, accepted