MEAP: Approximate optimal estimate extraction for the SMC-PHD filter

Tiancheng Li, Juan M. Corchado, Jesus Garcia, Javier Bajo

科研成果: 书/报告/会议事项章节会议稿件同行评审

3 引用 (Scopus)

摘要

Multi-estimate extraction (MEE), also referred to as displaying tracks, lies at the core of any multi-target tracking systems, but remains a challenge for the sequential Monte Carlo implementation of the probability hypothesis density (SMC-PHD) filter. In this paper, we recall decision and association techniques to distinguish real measurements of targets from clutter and to associate particles to measurements. The MEE problem is then formulated as a family of parallel single-estimate extraction problems, where the expected a posteriori (EAP) estimator can be employed, namely the multi-EAP (MEAP) estimator. The MEAP estimator is free of iterative clustering computation, computes fast and yields accurate and reliable estimates. Classical simulation scenarios are employed to demonstrate the superiority of the MEAP estimator over existing methods in terms of fast processing speed and best estimation accuracy.

源语言英语
主期刊名FUSION 2016 - 19th International Conference on Information Fusion, Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
2309-2316
页数8
ISBN(电子版)9780996452748
出版状态已出版 - 1 8月 2016
已对外发布
活动19th International Conference on Information Fusion, FUSION 2016 - Heidelberg, 德国
期限: 5 7月 20168 7月 2016

出版系列

姓名FUSION 2016 - 19th International Conference on Information Fusion, Proceedings

会议

会议19th International Conference on Information Fusion, FUSION 2016
国家/地区德国
Heidelberg
时期5/07/168/07/16

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