关键词: 粒子滤波, 无迹卡尔曼滤波, 重要性概率密度, 混合遗传模拟退火算法

Abstract:

The sample degeneracy is the critical problem existing in the particle filter. In order to solve this problem, a new combined particle filter algorithm, based on the genetic simulated annealing algorithm and unscented Kalman filter algorithm, is presented in this paper. In the proposed algorithm, unscented Kalman filter algorithm is used to generate the importance proposal distribution which can match the true posterior distribution more closely, and the genetic simulated annealing algorithm based upon the survival-of-the-fitness principle is applied to enhance the diversity of samples. Simulation results indicate the effectiveness and feasibility of the proposed algorithm.

Key words: particle filter, unscented Kalman filter, proposal probability density, genetic annealing simulated algorithm