Research on RAIM algorithm based on Gaussian mixture model under non-Gaussian noise

In this paper, the problem of poor fault detection performance of RAIM algorithm under non-Gaussian noise is studied. Firstly, the non-Gaussian distribution characteristics of observed noise are analyzed by using the measured data, and the probability density function of non-Gaussian noise is modeled by using the Gaussian mixture model, and the particle filter RAIM algorithm based on the Gaussian mixture distribution model is studied. The implementation process is as follows: Gaussian mixture distribution model is used to replace the Gaussian distribution used in the conventional particle filter RAIM algorithm process. At the same time, in order to improve the particle degradation and particle dilution problems existing in particle filter RAIM algorithm, firefly algorithm is introduced instead of resamping step, and an improved Gaussian mixture particle filter RAIM algorithm is proposed. Mathematical simulation shows that the improved algorithm can improve the accuracy of RAIM fault detection under non-Gaussian noise and shorten the fault alarm time of RAIM algorithm.