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Among the current high\u2010performance methods, the deep learning\u2010based methods require training samples, and their generalization ability is also limited by the training set. The separation of low\u2010rank and sparse matrix requires joint processing of multiple images with high computational complexity. The track\u2010before\u2010detect algorithms based on particle filtering also have high computational complexity. In this paper, the low\u2010rank and sparse matrix of a single image are proposed for target detection, and a differentiable objective function is used in the separation. At the same time, an extended multitarget tracking algorithm based on random sets is used for target filtering between frames, and the design of the filters adopts the conjugate distribution under the Bayesian framework. Finally, the practical infrared sequence images containing multiple targets and complex backgrounds were employed to verify the performance of the proposed algorithms by comparing them with state\u2010of\u2010the\u2010art algorithms.<\/jats:p>","DOI":"10.1049\/2024\/9914774","type":"journal-article","created":{"date-parts":[[2024,8,21]],"date-time":"2024-08-21T20:33:46Z","timestamp":1724272426000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Extended Infrared Target Filtering via Random Finite Set and Low\u2010Rank Matrix 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