A Dynamic-Data-Driven Method for Improving the Performance of Receiver Autonomous Integrity Monitoring

• Published in: IEEE access 2021, Vol.9, p.55833-55843
• Main Authors: Zheng, Xueen, Xu, Chengdong, Wang, Yudong, Zou, Haoming, Lv, Xijuan, Zhao, Shuai, Shi, Yansong, Shu, Qinghai
• Format: Article
• Language: English
• Subjects: alarm systems; Artificial neural networks; Domains; Error analysis; False alarms; Fault detection; Global navigation satellite system; Integrity; LPV-250; Model testing; Monitoring; multi-layer neural network; Neural networks; Particle swarm optimization; Probabilistic logic; Receiver autonomous integrity monitoring; Receivers; Satellite constellations; Satellites; Statistical analysis; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3070658

In this article, the problem of receiver autonomous integrity monitoring (RAIM) is transformed into a modeling problem using dynamic data and an artificial neural network. A new RAIM method based on a probabilistic neural network (P-RAIM) is presented to improve integrity monitoring performance. Compared with existing RAIM methods, P-RAIM has a greater ability to meet the monitoring requirements for localizer performance with vertical guidance down to altitudes of 250 feet (LPV-250) in a single global navigation satellite system. First, by projecting the pseudorange error model from the measurement domain into the positioning domain through multiconvolution, patterns including a satellite fault pattern and a fault-free pattern are obtained based on variance inflation theory. Second, the P-RAIM model is proposed as a modified dynamic-data-driven probabilistic neural network with five layers; moreover, unique methods for training sample collection and integrity support are presented. Then, particle swarm optimization is applied to optimize a fitness function based on the false alarm probability and missed detection probability thereby improving the ability of P-RAIM to meet the LPV-250 requirements, including the false alarm probability, missed detection probability, vertical alarm limit and alarm time. Finally, utilizing real satellite data from a receiver located in Beijing to verify the effectiveness and universality of P-RAIM, evaluation experiments show that both the false alarm probability and missed detection probability can be effectively reduced to meet the LPV-250 requirements when the positioning bias is no less than 40 m. Compared with least-squares-residuals RAIM, P-RAIM can more easily detect potential faulty satellites in a single constellation.