Gaussian mixture particle jump-markov-cphd fusion for multitarget tracking using sensors with limited views

Kai Da, Tiancheng Li, Yongfeng Zhu, Qiang Fu

科研成果: 期刊稿件文章同行评审

50 引用 (Scopus)

摘要

In this article, we propose a multisensor cardinalized probability density hypothesis (CPHD) filter for tracking an unknown number of targets that may maneuver over time by using a sensor network with partially overlapping fields of views (PO-FoVs). We develop a novel, Gaussian mixture particle (GMP) implementation of the jump-Markov CPHD filter to deal with highly non-linear/non-Gaussian models and target maneuvers. The concepts of zero-forcing and zero-Avoiding originally used in density approximation are introduced to elucidate a key difference between geometric and arithmetic averaging approaches, which are extended for joint target-state and mode fusion with regard to each PO-FoV for which distributed flooding is used for internode communication. The resulting GMP-JMCPHD fusion algorithm comprises three FoV-oriented steps: splitting, fusion, and merging. Simulations are provided to demonstrate the effectiveness of the proposed approaches.

源语言英语
文章编号9166749
页(从-至)605-616
页数12
期刊IEEE Transactions on Signal and Information Processing over Networks
6
DOI
出版状态已出版 - 2020

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