Multi-EAP: Extended EAP for multi-estimate extraction for SMC-PHD filter

Tiancheng Li, Juan M. Corchado, Shudong Sun, Hongqi Fan

科研成果: 期刊稿件文章同行评审

19 引用 (Scopus)

摘要

The ability to extract state-estimates for each target of a multi-target posterior, referred to as multi-estimate extraction (MEE), is an essential requirement for a multi-target filter, whose key performance assessments are based on accuracy, computational efficiency and reliability. The probability hypothesis density (PHD) filter, implemented by the sequential Monte Carlo approach, affords a computationally efficient solution to general multi-target filtering for a time-varying number of targets, but leaves no clue for optimal MEE. In this paper, new data association techniques are proposed to distinguish real measurements of targets from clutter, as well as to associate particles with measurements. The MEE problem is then formulated as a family of parallel single-estimate extraction problems, facilitating the use of the classic expected a posteriori (EAP) estimator, namely the multi-EAP (MEAP) estimator. The resulting MEAP estimator is free of iterative clustering computation, computes quickly and yields accurate and reliable estimates. Typical simulation scenarios are employed to demonstrate the superiority of the MEAP estimator over existing methods in terms of faster processing speed and better estimation accuracy.

源语言英语
页(从-至)368-379
页数12
期刊Chinese Journal of Aeronautics
30
1
DOI
出版状态已出版 - 1 2月 2017
已对外发布

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