The Research And Improvement Of Filtering RAIM Algorithm

With the rapid development of Global Navigation Satellite System(GNSS),integrity has become an important service performance in navigation satellite system.Receiver autonomous integrity monitoring(RAIM)is an essential part of navigation satellite system integrity,which uses the redundant observations of receiver to achieve the detection and isolation of fault satellites.It's quick and easy to implement without external devices and it has become an important process in navigation.This paper researches the application and improvement of filtering RAIM algorithm.The main study of the following:(1)the improvement of Kalman filter based RAIM for detecting micro and slowly growing pseudo-range bias;(2)the RAIM algorithm based on improved particle filter under the condition of non-Gaussian observation noise.First of all,this paper summarizes the research status of RAIM at home and abroad.Then,introduces the theoretical basis of RAIM,including the GNSS navigation and positioning error,the theory and method of hypothesis testing,the main index of integrity and the RAIM process.Thirdly,introduce the principle of GNSS pseudorange location and the least square location algorithm,and the GNSS filtering location algorithms is derivated,including extended Kalman filter location algorithm,robust extended Kalman filter location algorithm,and particle filter location algorithm.Fourthly,study the improvement of Kalman filter based RAIM.The conventional Kalman filter RAIM algorithm is derivated,and the influence of micro and slowly growing pseudo-range bias on RAIM is analyzed.On this basis,the innovation extrapolation method and the accumulated epoches method is combined and an extrapolation-accumulation RAIM algorithm is proposed.The test statistics of innovation extrapolation method in several epochs is accumulated,thus it has a better ability in detecting micro and slowly growing bias.Meanwhile,the pseudo-range bias is corrected by robust extended Kalman filter(REKF).The results of both simulation experiment and the measured data experiment show that,the extrapolation-accumulation RAIM algorithm has a higher fault detection rate for micro pseudo-range bias and shorter detection time-delay for slowly growing pseudo-range bias,and the position accuracy is improved after correcting the pseudo-range bias.Finally,study the RAIM algorithm based on robust extended Kalman particle filter(REK-PF).The importance density function of particle filter is calculated by robust extended Kalman filter in order to improve the accuracy of state estimation when pseudo-range bias exists,and the particle degradation is restrained.On this basis,the smoothed residual test statistics is set up for satellite fault detection and isolation.The results of both simulation experiment and the measured data experiment show that,RAIM algorithm based on REK-PF can well detect and isolate the faulty satellite under the condition of non-Gaussian observation noise.Compared to the RAIM algorithm based on convention particle filter,RAIM algorithm based on REK-PF has a better performance on fault detection,and its position accuracy is improved.