Performance Evaluation of Fault Detection Algorithms as Applied to Automotive Localisation
Résumé
A very important parameter to measure the quality of localisation systems is integrity in Global Navigation Satellite System (GNSS). Within this perspective, Integrity first considers the removal of outliers at the receiver level. It is typically monitored applying Fault Detection and Exclusion (FDE) algorithms. Then, integrity ensures a high probability bounded position error by computing protection levels. However, their performance depends very much on the faults characteristics and data redundancy which reflect the environment in which location estimates are made, and thus differ from scenario to scenario. After analyzing the specificities of GNSS based positioning in the automotive context, and the mechanism of the Receiver Autonomous Integrity Monitoring (RAIM) algorithm, a novel Kalman Filter based FDE algorithm is proposed in this paper. This is validated using hybrid simulation techniques based on real data from vehicle proprioceptive sensors and partially simulated GNSS data, where satellite configuration and faults characteristics can be controlled. The chosen scenario is that found in urban environments, representative of areas where localisation systems suffer many difficulties when mounted on land platforms. A comparison between the RAIM and the new algorithm is included.
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