针对基于贝叶斯原理的序贯蒙特卡罗粒子滤波器出现退化现象的原因, 以无敏粒子滤波(U PF)、辅助粒子滤波

(A S IR) 及采样重要再采样(S IR) 等改进的粒子滤波算法为例, 对消除该缺陷的关键技术(优化重要密度函数及再采样) 进行了

分析研究。说明通过提高重要密度函数的似然度、引进当前测量值、预增和复制大权值粒子等方式, 可以有效改善算法性能。

最后通过对一无源探测定位问题进行仿真, 验证了运用该关键技术后, 算法的收敛精度和鲁棒性得到进一步增强。-　Abstract:W e analyze the degeneracy phenomenon of sequen t ialMon te Carlo part icle f ilters based on

bayesian theo rem , pu t focu s on the key techn iques ( good cho ice of impo rtance den sity and u se of

resamp ling ) to reduce it s effect s. Several imp roving schemes such as the U n scen ted Part icle F ilters

(U PF) , the A ux iliary Samp ling Impo rtance Resamp ling (A S IR ) and the Samp ling Impo rtance Resamp ling

(S IR ) algo rithm s are in t roduced to illu st rate th rough increasing the likelihood of the impo rtance den sity o r

inco rpo rat ing new measu remen t, o r rep licat ing part icles w ith large w eigh t s w ith in the generic f rame of

part icle f ilters, the convergence accu racy and robu stness behavio rs of the algo rithm can be effect ively

imp roved. A typ ical passive detect ion and locat ion p rob lem is simu lated to p rove above conclu sion s.