A random finite set based joint probabilistic data association filter with non-homogeneous Markov ch

• 作 者: Yun ZHU Shuang LIANG Xiaojun WU Honghong YANG
• 机 构: KeyLaboratoryofModernTeachingTechnology,SchoolofComputerScience,AcademyofAdvancedInterdisciplinaryRe
• 出 处: 信息与电子工程前沿:英文版
• 发表日期: 2021年8期
• 关键词: Target tracking Filtering theory Random finite set theory Bayes methods Markov c
• 基金项目: Project supported by the National Key Research and Development Program of China(No.2017YFB1402102),the National Natural Science Foundation of China(Nos.61907028 and 11872036),the Natural Science Found

We demonstrate a heuristic approach for optimizing the posterior density of the data association tracking algorithm via the random finite set(RFS)theory.Specifically,we propose an adjusted version of the joint probabilistic data association(JPDA)filter,kn