聚合重采样粒子滤波的机载无源定位算法  

Merging Resampling Particle Filter for Air-borne Passive Location

作　　者：刘学[1,2] 张志敏[1] 焦淑红[2] 

机构地区：[1]中国人民解放军91245部队 [2]哈尔滨工程大学信息与通信工程学院

出　　处：《应用基础与工程科学学报》2013年第3期581-592,共12页Journal of Basic Science and Engineering

基　　金：973国家安全重大基础研究基金资助课题(61393010101-1);黑龙江省教育厅科学技术研究项目(11544025)

摘　　要：为了加快无源定位速度、提高定位精度,提出一种基于聚合重采样粒子滤波的机载无源定位算法,首先利用基于离散状态空间的粒子聚合技术对空间相近粒子进行加权聚合,在保证粒子空间分布合理性的同时有效抑制了粒子的退化;然后采用Thompson-Taylor算法的思想随机线性组合生成新的采样样本,优化粒子在状态空间中的分布特性,增加了样本的多样性,提高滤波精度,最后将所提算法应用于机载无源定位系统,仿真结果验证了该算法的有效性.To meet the requirements of high location speed and accuracy in the air-borne passive location, a merging resampling particle filter was proposed. Firstly, the particle merging technique based on particles＇ spatial similarity was used to keep rational distribution and restrain the weight degeneracy problem of the particles. Secondly, the Thompson-Taylor_ algorithm was chosen to generate new particles in the state space to explore the state space more efficiently, so that the diversity of the samples was increased and the precision of the filter was improved. Finally, simulation results showed the availability of this method.

关 键 词：无源定位 粒子滤波 粒子聚合 重采样 状态估计 

分 类 号：TN958.57[电子电信—信号与信息处理]