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.
Highlights
E sampled particles will seriously deviate from the actual target position, reducing the accuracy of the state prediction mean value and covariance
In order to enhance the filter capability of particle filter algorithm for nonlinear environmental measurement data, the influence of measurement information, model error, and noise interference should be fully considered in the design of importance density function during APF sampling and resampling process
This paper proposes a new improved auxiliary particle filter (IAPF), and it is applied to the navigation position system. is filter algorithm designs an appropriate importance density function to carry out importance sampling, which could take the latest measurement information into account and adjust adaptively the particle weight distribution
Summary
E sampled particles will seriously deviate from the actual target position, reducing the accuracy of the state prediction mean value and covariance. In order to enhance the filter capability of particle filter algorithm for nonlinear environmental measurement data, the influence of measurement information, model error, and noise interference should be fully considered in the design of importance density function during APF sampling and resampling process.
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