本文關(guān)鍵詞： 粒子濾波 無(wú)跡卡爾曼濾波 優(yōu)化組合策略 距離判決　出處：《計(jì)算機(jī)工程與科學(xué)》2017年08期 　論文類型：期刊論文

【摘要】：針對(duì)標(biāo)準(zhǔn)粒子濾波算法存在的粒子退化與貧化問(wèn)題,提出了一種新的改進(jìn)粒子濾波算法。該算法采用無(wú)跡卡爾曼濾波、優(yōu)化組合策略和標(biāo)準(zhǔn)粒子濾波相結(jié)合的方法,運(yùn)用UKF產(chǎn)生重要性密度函數(shù),解決標(biāo)準(zhǔn)PF算法中以先驗(yàn)概率密度函數(shù)作為建議分布所引發(fā)的退化問(wèn)題;運(yùn)用優(yōu)化組合重采樣策略保證所有粒子的信息以一定概率得到繼承,維持粒子集中粒子的多樣性。理論分析與仿真結(jié)果均表明,改進(jìn)算法能有效地解決標(biāo)準(zhǔn)粒子濾波存在的粒子退化問(wèn)題并避免粒子貧化現(xiàn)象的出現(xiàn),具有更高的狀態(tài)估計(jì)精度。
[Abstract]:Aiming at the problem of particle degradation and dilution in standard particle filtering algorithm, a new improved particle filter algorithm is proposed, which uses unscented Kalman filter. Combining the optimal combination strategy and standard particle filter, using UKF to generate the importance density function, the degradation problem caused by the priori probability density function as the suggested distribution in the standard PF algorithm is solved. The optimal combinatorial resampling strategy is used to ensure that the information of all particles is inherited with a certain probability, and the diversity of particles in the particle concentration is maintained. The theoretical analysis and simulation results show that. The improved algorithm can effectively solve the particle degradation problem of standard particle filter and avoid the phenomenon of particle dilution. It has higher state estimation accuracy.
【作者單位】： 空軍工程大學(xué)信息與導(dǎo)航學(xué)院;空軍工程大學(xué)防空反導(dǎo)學(xué)院;
【分類號(hào)】：TN713
【正文快照】： 1引言在線性假設(shè)和高斯噪聲背景條件下,卡爾曼濾波是雷達(dá)目標(biāo)跟蹤的最佳算法,而在現(xiàn)實(shí)世界中,目標(biāo)模型大多是非線性非高斯的,常用的算法有擴(kuò)展卡爾曼濾波EKF(Extended Kalman Filter)、無(wú)跡卡爾曼濾波UKF(Unscented Kalman Filter)以及粒子濾波PF(Particle Filter)。其中粒子，

本文編號(hào)：1466029