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However, when tracking multiple targets in dense clutters, the computational complexity of the traditional labeled multi\u2010Bernoulli filter will increase exponentially. A labeled multi\u2010Bernoulli tracking algorithm based on maximum likelihood recursive update is proposed, which can reduce the computational scale while maintaining tracking accuracy. Specifically, when performing posterior estimation, a maximum likelihood recursive update method is proposed to replace the complete enumeration, truncated enumeration, or sampling enumeration methods used in many traditional methods. Furthermore, combined with the Gaussian mixture technique, a maximum likelihood recursive updating labeled multi\u2010Bernoulli tracking algorithm is constructed. Simulation results demonstrated that the proposed filter obtained a good balance between the tracking accuracy and computational efficiency.<\/jats:p>","DOI":"10.1049\/2024\/1994552","type":"journal-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T10:34:01Z","timestamp":1726050841000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Labeled Multi\u2010Bernoulli Filter Based on Maximum Likelihood Recursive Updating"],"prefix":"10.1049","volume":"2024","author":[{"given":"Yuhan","family":"Song","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7005-5672","authenticated-orcid":false,"given":"Han","family":"Shen-Tu","sequence":"additional","affiliation":[]},{"given":"Junhao","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Yizhen","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Yunfei","family":"Guo","sequence":"additional","affiliation":[]}],"member":"265","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"volume-title":"Tracking and Data Association","year":"1988","author":"Bar-Shalom Y.","key":"e_1_2_12_1_2"},{"doi-asserted-by":"crossref","unstructured":"SingerR. 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