A New Threshold-Free RAIM Based on Monte Carlo Sampling

The detection threshold of traditional RAIM needs to be manually determined based on experience and debugging, which limits its performance and adaptability. In this paper, the Monte Carlo method is used to solve the problem, and a new threshold-free RAIM is proposed. Different from the traditional RAIM, the new method estimates the inlier probabilities of measurements through the discretization sampling and integration of the pseudo-range residuals. The probabilities represent the quality of measurements, and can be used to construct the weights of satellites. Further, the WLS algorithm is used to improve the solution. GNSS positioning experiments under different conditions are carried out. In the static condition, compared with the LS, the 3D-RMS of the threshold-free RAIM is reduced by 44.1% in the case of the single GPS constellation, and 40.2% of the GPS + BDS dual-constellation; compared with the best configured RAIM, the above two values are 15.7% and 20.1%, respectively. In the dynamic condition, compared with the LS, the 3D-RMS of the threshold-free RAIM is reduced by 34.2% in the case of the single GPS constellation, and 9.8% of the GPS + BDS dual-constellation; compared with the best configured RAIM, these two values are 7.6% and 8.6%, respectively.