关键词: 粒子滤波, 权值退化, 粒子流滤波, 新息误差, Galerkin有限元法

Abstract: There exist some problems in the process of Particle Filter (PF), such as particle weight degeneracy, curse of dimensionality, and high computational cost. By constructing a logarithmic homotopy function, particle flow filter can avoid the problem of particle weight degeneracy, but it relies on the observation equation too much when solving the boundary value problem, and has poor effect when the noise is high. To address these problems, an improved particle flow filter algorithm was proposed. Firstly, an "innovation error" structure was introduced into the process of particle flow, so that the update of each particle is independent. Then, the Galerkin finite element method was utilized to obtain the numerical solution of the boundary value problem, so as to avoid the numerical instability problem that may be caused by the fitting sample prior. Finally, the performance of the improved algorithm was tested in the common nonlinear filter model and the maneuvering target tracking model. Simulation results show that the improved algorithm can suppress the dependency of the system on observation information, and has relatively good results with increasing noise, which effectively improve the filtering accuracy, and in multi-dimensional target tracking cases, the algorithm's computational efficiency and filtering accuracy are higher than those of the standard particle filter.

Key words: particle filter, weight degeneracy, particle flow filter, innovation error, Galerkin finite element method