Particle Filter Algorithm Based on Hybrid Multi-Strategy Optimization

The standard particle filter has the problem of particle impoverishment, while dealing with nonlinear problems requires many particles to achieve the required estimation accuracy, so the standard particle filter reduces the comprehensive performance of algorithm. This paper proposed a hybrid multi-strategy optimization particle filtering algorithm, which combines Levy flight strategy, differential evolution algorithm and success history strategy. The method firstly defines the basic framework of the sample set with Levy flight strategy, and optimizes the low-weight invalid particles with the differential evolution algorithm. Then the successful history strategy was used to adjust the parameters adaptively, to dynamically adjust the algorithm’s optimum length, so as to guide more particles to the high likelihood region. Simulation results show that the proposed algorithm can effectively improve the particle diversity and filtering accuracy, enhance the particle impoverish problem under low measurement noise, and reduce the number of particles required for nonlinear system estimation.