{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T09:53:44Z","timestamp":1772704424457,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T00:00:00Z","timestamp":1620950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper presents a new group target tracking method based on the standard multi-sensor multi-target multi-Bernoulli (MS-MeMBer) filter. In the prediction step, the group structure is used to constrain the movement of the constituent members within the respective groups. Specifically, the group of members is considered as an undirected random graph. Combined with the virtual leader-follower model, the motion equation of the members within groups is formulated. In the update step, the partitioning problem of multiple sensors is transformed into a multi-dimensional assignment (MDA) problem. Compared with the original two-step greedy partitioning mechanism, the MDA algorithm achieves better measurement partitions in group target tracking scenarios. To evaluate the performance of the proposed method, a simulation scenario including group splitting and merging is established. Results show that, compared with the standard MS-MeMBer filter, our method can effectively estimate the cardinality of members and groups at the cost of increasing computational load. The filtering accuracy of the proposed method outperforms that of the MS-MeMBer filter.<\/jats:p>","DOI":"10.3390\/rs13101920","type":"journal-article","created":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T02:31:34Z","timestamp":1621218694000},"page":"1920","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Group Target Tracking Based on MS-MeMBer Filters"],"prefix":"10.3390","volume":"13","author":[{"given":"Zhiguo","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7184-5057","authenticated-orcid":false,"given":"Jinping","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Huiyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Leicester, Leicester LE1 7RH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2648-7227","authenticated-orcid":false,"given":"Congan","family":"Xu","sequence":"additional","affiliation":[{"name":"Information Fusion Institute, Naval Aviation University, Yantai 264001, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cheng, X., Song, L., and Zou, Z. 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