Study on stochastic perturbation strong tracking particle filter

Abstract

To solve the degeneracy phenomenon and to improve the ability for tracking the breaking states are two difficult problems in the application of particle filter. Sequential important re-sampling can reduce orilliminate degeneracy, but the sample impoverishment is a secondary result. Extended particle filter can also reduce the degeneracy, but it cannot track the breaking states. The ability to track the breaking states can be improved by a strong tracking particle filter, but the degeneracy phenomenon will not be well solved still. A stochastic perturbation strong tracking particle filter is proposed for solving the above problems, in which a stochastically perturbative re-sampling is introduced into a strong tracking particle filter. Thus a stochastic perturbation is added to the particle with maximal weight to form some new particles, and the degenerative particles are displaced by the new particles to solve the degeneracy phenomenon and so the sample impoverishment improves the diversity of the samples. The ability of the proposed algorithm to track breaking states is also improved, and the feasibility and validity of the proposed algorithm are demonstrated by the simulation results of the standard validation model and the system with constants in different periods of time.