An adaptive optimized strategy for particle filter

Particle filter has been widely applied into many fields in recent years. Combined with the deficiency analysis of particle filter, an adaptive optimized strategy for particle filter is proposed. This adaptive optimized strategy includes two parts. One is an improved hybrid proposal distribution with adaptive parameter optimization for particle filter. Based on the performance analysis of different proposal distribution, a hybrid proposal distribution with adaptive annealing parameter optimization is utilized to consider current information of the latest observed measurement. The other is an adaptive resampling strategy based on diversity guidance. An adaptive resampling step in particle filter is tuned based on two diversity measures and an improved partial stratified resampling strategy is presented based on the weights optimal idea. With the simulation program, the performance of the proposed strategy is evaluated and its validity is verified.