GPS receiver autonomous integrity monitoring method

Abstract

The invention belongs to the technical field of satellite navigation receivers, and provides a GPS receiver autonomous integrity monitoring method, which comprises the following steps: S1, acquiring coordinates of a GPS receiver; S2, selecting a main particle filter and an auxiliary particle filter, and determining an observation vector of each particle filter; S3, updating, improving and normalizing the particle weight of each particle filter through a pigeon inspired algorithm; S4, obtaining a log-likelihood ratio test statistic according to the particle weight of each particle filter; and S5, obtaining a judgment function according to the log-likelihood ratio test statistics, and carrying out fault detection and isolation. According to the method, PF particles are optimized and adjusted through the pigeon inspired algorithm, so that the number of effective particles is increased, the diversity of the particles is improved, and the problem of particle depletion is solved.

Description

technical field [0001] The invention relates to the technical field of satellite navigation receivers, in particular to a GPS receiver autonomous integrity monitoring method. Background technique [0002] GPS is the most widely used global satellite navigation system in the world today, and it has been successfully used in navigation equipment and track recording equipment of various vehicles such as aviation, marine and land vehicles. For global satellite positioning and navigation systems represented by GPS, especially in navigation systems with very high reliability requirements such as airborne electronic navigation equipment, integrity testing is an indispensable link in the development of related systems. The research on Receiver Autonomous Integrity Monitoring (RAIM) method based on the consistency check of redundant ranging information has become the current GPS fault diagnosis and alarm and integrity method because of its advantages of rapid response, simple impleme...

AI Technical Summary

Problems solved by technology

[0003] The research on the GPSRAIM method mainly focuses on fault detection and isolation (fault detection and isolation, FDI) technology based on redundant information. The related algorithms include pseudorange comparison method, least square residual method, parity vector method, etc. Efficient detection of small faults

In addition, the traditional Kalman filter can only deal with the Gaussian system well, otherwise the fault estimation performance will be degraded, and the signal, noise and interference in the GPS receiver system are not standard Gaussian distribution, only using the traditional Kalman filter will affect the accuracy of RAIM sex

In response to this problem, although the RAIM algorithm based on the particle filter (Particle filter, PF) and log likelihood ratio consistency test was proposed, using the suboptimal estimation ability of PF for non-Gaussian and nonlinear systems, PF itself has some Defects, such as particle degradation and particle depletion issues, seriously affect the estimation accuracy of PF

Although there are many RAIM-related works to improve PF, such as particle swarm optimization (PSO) and chaotic particle swarm optimization to improve PF particles, the PSO algorithm also has problems such as slow convergence speed and easy to fall into local optimum.

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