Track-before-detect for Infrared Maneuvering Dim Multi-target via MM-PHD
- 作者：Yunli LONG feiyunlyi@126.com ; [Author Vitae] ; Hui XU simon863@vip.sina.com ; [Author Vitae] ; Wei AN ; nudtanwei@tom.com ; Li LIU
- 关键词：target tracking ; probability hypothesis density ; Monte Carlo ; track-before-detect ; importance re-sampling
- 刊名：Chinese Journal of Aeronautics
- 出版年：2012
- 期刊代码：3_10009361
- 类别：et
- 出版时间：April, 2012
- 卷：25
- 期：2
- 页码：252-261
- 文件大小：906 K
摘要
In this paper, we present a novel and efficient track-before-detect (TBD) algorithm based on multiple-model probability hypothesis density (MM-PHD) for tracking infrared maneuvering dim multi-target. Firstly, the standard sequential Monte Carlo probability hypothesis density (SMC-PHD) TBD-based algorithm is introduced and sequentially improved by the adaptive process noise and the importance re-sampling on particle likelihood, which result in the improvement in the algorithm robustness and convergence speed. Secondly, backward recursion of SMC-PHD is derived in order to ameliorate the tracking performance especially at the time of the multi-target arising. Finally, SMC-PHD is extended with multiple-model to track maneuvering dim multi-target. Extensive experiments have proved the efficiency of the presented algorithm in tracking infrared maneuvering dim multi-target, which produces better performance in track detection and tracking than other TBD-based algorithms including SMC-PHD, multiple-model particle filter (MM-PF), histogram probability multi-hypothesis tracking (H-PMHT) and Viterbi-like.