J Shanghai Jiaotong Univ Sci ›› 2022, Vol. 27 ›› Issue (5): 671-679.doi: 10.1007/s12204-022-2458-x

• Automation System & Theory • Previous Articles     Next Articles

A Class of Distributed Variable Structure Multiple Model Algorithm Based on Posterior Information of Information Matrix

HUANG Yinghao1,2(黄颖浩), WU Yi3(吴怡), YAO Lixiu2 (姚莉秀), CAI Yunze1,2∗ (蔡云泽)   

  1. (1. Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Department of Automation; Key Laboratory of System Control and Information Processing of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China; 3. Alibaba (China) Co., Ltd., Hangzhou 311121, China)
  • Received:2021-03-24 Online:2022-09-28 Published:2022-09-03

Abstract: The tracking of maneuvering targets in radar networking scenarios is studied in this paper. For the interacting multiple model algorithm and the expected-mode augmentation algorithm, the fixed base model set leads to a mismatch between the model set and the target motion mode, which causes the reduction on tracking accuracy. An adaptive grid-expected-mode augmentation variable structure multiple model algorithm is proposed. The adaptive grid algorithm based on the turning model is extended to the two-dimensional pattern space to realize the self-adaptation of the model set. Furthermore, combining with the unscented information filtering, and by interacting the measurement information of neighboring radars and iterating information matrix with consistency strategy, a distributed target tracking algorithm based on the posterior information of the information matrix is proposed. For the problem of filtering divergence while target is leaving radar surveillance area, a k-coverage algorithm based on particle swarm optimization is applied to plan the radar motion trajectory for achieving filtering convergence.

Key words: radar network system, variable structure multiple model, consistency filtering, information matrix, particle swarm optimization

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