{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T14:49:22Z","timestamp":1772722162764,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,6]],"date-time":"2018-11-06T00:00:00Z","timestamp":1541462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Key Research and Development Program of China","award":["2016YFE0111900"],"award-info":[{"award-number":["2016YFE0111900"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To deal with the problem of multitarget tracking with measurement origin uncertainty, the paper presents a multitarget tracking algorithm based on Adaptive Network Graph Segmentation (ANGS). The multitarget tracking is firstly formulated as an Integer Programming problem for finding the maximum a posterior probability in a cost flow network. Then, a network structure is partitioned using an Adaptive Spectral Clustering algorithm based on the Nystr\u00f6m Method. In order to obtain the global optimal solution, the parallel A* search algorithm is used to process each sub-network. Moreover, the trajectory set is extracted by the Track Mosaic technique and Rauch\u2013Tung\u2013Striebel (RTS) smoother. Finally, the simulation results achieved for different clutter intensity indicate that the proposed algorithm has better tracking accuracy and robustness compared with the A* search algorithm, the successive shortest-path (SSP) algorithm and the shortest path faster (SPFA) algorithm.<\/jats:p>","DOI":"10.3390\/s18113791","type":"journal-article","created":{"date-parts":[[2018,11,7]],"date-time":"2018-11-07T03:45:22Z","timestamp":1541562322000},"page":"3791","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multitarget Tracking Algorithm Based on Adaptive Network Graph Segmentation in the Presence of Measurement Origin Uncertainty"],"prefix":"10.3390","volume":"18","author":[{"given":"Tianli","family":"Ma","sequence":"first","affiliation":[{"name":"Autonomous Systems and Intelligent Control International Joint Research Center, Xi\u2019An Technological University, Xi\u2019an 710021, China"},{"name":"School of Mechatronic Engineering, Xi\u2019An Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Gao","sequence":"additional","affiliation":[{"name":"Autonomous Systems and Intelligent Control International Joint Research Center, Xi\u2019An Technological University, Xi\u2019an 710021, China"},{"name":"School of Mechatronic Engineering, Xi\u2019An Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaobo","family":"Chen","sequence":"additional","affiliation":[{"name":"Autonomous Systems and Intelligent Control International Joint Research Center, Xi\u2019An Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoru","family":"Song","sequence":"additional","affiliation":[{"name":"Autonomous Systems and Intelligent Control International Joint Research Center, Xi\u2019An Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Challa, S., Morelande, M.R., Mu\u0161icki, D., and Evans, R.J. 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