Improved Auxiliary Particle Filter for SINS/SAR Navigation

Abstract

In order to obtain the relatively appropriate importance density function and alleviate the problem of particle degradation, a new improved auxiliary particle filter algorithm is proposed. After calculating the auxiliary variable, the adaptive regulator is employed to obtain the state estimation. So, the latest measurement information is efficiently utilized to establish a better importance density function in the importance sampling process. Then, the process of particle weights’ adaptive adjustment and random-weighted calculation can keep the diversity of particles and improve the filter precision; thus, it can better solve the filter problem of nonlinear system model error and noise interference. The simulation and analysis result show that the proposed algorithm can optimize the filter performance and improve the calculation precision in the positioning of the SINS/SAR integrated navigation system, compared with the other two existing filters.