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