粒子群优化粒子滤波方法

针对粒子滤波方法存在粒子贫乏以及初始状态未知时需要大量粒子才能进行鲁棒状态预估等问题，将粒子群优化思想引入粒子滤波中．该方法将最新观测值融合到采样过程中，并对采样过程利用粒子群优化算法进行优化．通过优化，可使粒子集朝后验概率密度分布取值较大的区域运动，从而克服了粒子贫乏问题，并极大地降低了精确预估所需的粒子数．实验结果表明，该算法具有较高的预估精度和较好的鲁棒性．

Particle swarm optimized particle filter

To the problem of particle impoverishment and needing a large sample size for robust state estimation when initial state is unknown,particle swarm optimization(PSO) is introduced into generic particle filter.Particle swarm optimized particle filter(PSOPF) incorporates the newest observations into sampling process and also optimizes it.Through PSO,particles are moved towards regions where they have larger values of posterior density function.As a result,the impoverishment of particle filter is overcomed and the sample size necessary for accurate state estimation is reduced dramatically.Experimental results show that PSOPF has higher estimation accuracy and better robustness.