文章基本信息

标题：Adaptive Target Birth Intensity for ET-PHD Filter
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作者：Lu Miao ; Xin-xi Feng ; Luo-jia Chi 等
期刊名称：MATEC Web of Conferences
电子版ISSN：2261-236X
出版年度：2018
卷号：176
DOI：10.1051/matecconf/201817603010
语种：English
出版社：EDP Sciences
摘要：An adaptive tracking algorithm based on Extended target Probability Hypothesis Density (ETPHD) filter is proposed for extended target tracking problem with priori unknown target birth intensity.The algorithm is implemented by gaussian mixture, where the target birth intensity is generated by measurement-driven, and the persistent and the newborn targets intensity are respectively predicted and updated. The simulation results show that the proposed algorithm improves the performance of the probability hypothesis density filter in the extended target tracking.