Abstract: Traditional Particle Filter (PF) algorithm based on Particle Swarm Optimization (PSOPF), which moves the moving particles to the high likelihood region, destroys the prediction distribution. When the likelihood function has many peaks, it has a large computation amount while filtering performance does not improved significantly. To solve this problem, a new PSOPF based on the Adjustment of the Likelihood (LA-PSOPF) was proposed. Under the premise of preserving the prediction distribution, the Particle Swarm Optimization (PSO) algorithm was used to adjust the likelihood distribution to increase the number of effective particles and improve the filtering performance. Meanwhile, a strategy of local optimization was introduced to scale down the swarm of PSO, reduce the amount of calculation and achieve the balance of accuracy and speed of estimation. The simulation results show that the proposed algorithm is better than PF and PSOPF when the measurement error is small and the likelihood function has many peaks, and the computing time is less than that of PSOPF.

Key words: Particle Filter (PF), Particle Swarm Optimization (PSO), prediction density, likelihood function, local optimization

摘要： 传统基于粒子群优化的粒子滤波（PF）算法（PSOPF）在移动粒子向高似然区域移动的过程中，由于破坏了预测分布，当似然函数具有多峰时，其在具有大计算量的同时滤波性能并没有明显提升。针对该问题，提出了基于似然分布调整的粒子群优化粒子滤波新方法（LA-PSOPF）。在保留预测分布的前提下，运用PSO算法调整似然分布，提高有效粒子数量，进而提高滤波性能；同时引入局部优化策略，缩减参与PSO优化的粒子群规模，从而减少运算量，达到滤波精度与速度的平衡。仿真结果表明，当量测误差较小，似然函数具有多峰值时，改进算法的滤波精度和稳定性都优于PF算法和PSOPF算法，同时运算时间少于PSOPF算法。

关键词: 粒子滤波, 粒子群优化, 预测分布, 似然函数, 局部优化