Efficient Particle Swarm Optimized Particle Filter Based Improved Multiple Model Tracking Algorithm. (4th January 2016)

• Record Type: Journal Article
• Authors: Chen, Zhimin; Qu, Yuanxin; Xi, Zhengdong; Bo, Yuming; Liu, Bing
• Is Part Of: Computational intelligence. Volume 33:Number 2(2017)
• Page Start: 262
• Page End: 279
• Publication Date: 2016-01-04
• Subjects: particle filter -- maneuvering target tracking -- noninteracting multiple model -- enhanced particle swarm; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12084
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract : To meet the requirements of modern radar maneuvering target tracking system and remedy the defects of interacting multiple model based on particle filter, noninteracting multiple model (NIMM) and enhanced particle swarm optimized particle filter (EPSO‐PF) are proposed. The improved maneuvering target tracking algorithm (NIMM‐EPSO‐PF) in this article combines the advantages of NIMM with those of EPSO‐PF. NIMM is used to figure out the index of particles to avoid the high computing complexity resulting from particle interaction, and EPSO‐PF can not only improve the equation of particle update through the rules individuals develop an understanding of group but also enhance particle diversity and accuracy of particle filter through the small variation probability of superior velocity. Besides, the random assignment of inferior velocity is capable of upgrading filter efficiency. As shown by the experimental result, the NIMM‐EPSO‐PF not only improves target tracking accuracy but also maintains high real‐time performance. Therefore, the improved algorithm can be applied to modern radar maneuvering target tracking field efficiently.