{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:21:15Z","timestamp":1760242875334,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2016,10,11]],"date-time":"2016-10-11T00:00:00Z","timestamp":1476144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science fund of China","award":["61374159","61135001","61374023"],"award-info":[{"award-number":["61374159","61135001","61374023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, an adaptive collaborative Gaussian Mixture Probability Hypothesis Density (ACo-GMPHD) filter is proposed for multi-target tracking with automatic track extraction. Based on the evolutionary difference between the persistent targets and the birth targets, the measurements are adaptively partitioned into two parts, persistent and birth measurement sets, for updating the persistent and birth target Probability Hypothesis Density, respectively. Furthermore, the collaboration mechanism of multiple probability hypothesis density (PHDs) is established, where tracks can be automatically extracted. Simulation results reveal that the proposed filter yields considerable computational savings in processing requirements and significant improvement in tracking accuracy.<\/jats:p>","DOI":"10.3390\/s16101666","type":"journal-article","created":{"date-parts":[[2016,10,11]],"date-time":"2016-10-11T11:27:28Z","timestamp":1476185248000},"page":"1666","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking"],"prefix":"10.3390","volume":"16","author":[{"given":"Feng","family":"Yang","sequence":"first","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"Key Laboratory of Information Fusion Technology, Ministry of China, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongqi","family":"Wang","sequence":"additional","affiliation":[{"name":"Southwest China Research Institute of Electronic Equipment (SWIEE), Chengdu 610036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"Key Laboratory of Information Fusion Technology, Ministry of China, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"Key Laboratory of Information Fusion Technology, Ministry of China, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"Key Laboratory of Information Fusion Technology, Ministry of China, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,10,11]]},"reference":[{"key":"ref_1","unstructured":"Blackman, S.S., and Popoli, R. 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