确定性核粒子群的粒子滤波跟踪算法及其CRLB推导

Deterministic core particle swarm and derivation of CRLB in particle filter tracking algorithm

针对运动声阵列在有色噪声环境中的非线性滤波跟踪问题,提出一种确定性核粒子群的粒子滤波算法.该算法通过确定性初始化核粒子集、确定性后验概率密度函数及粒子群与核粒子集更新方式来提高跟踪的精度,并推导出该算法的理论误差性能下界.与传统的粒子滤波算法相比,仿真结果表明了所提出算法的有效性和优越性.

In order to study the nonlinear filter tracking problem of dynamic acoustic array in colored noise environment, the deterministic core particle swarm particle filter algorithm is proposed.The accuracy of the maneuvering target tracking is obviously enhanced by initialized deterministic core particle,deterministic probability density function and the renewed method of particle swarms and core particle,and the Cramer Rao low bound（CRLB） is also deduced.Compared with the traditional particle filter algorithm,the simulation results show the effectiveness and superiority of the presented algorithm.