{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T07:30:27Z","timestamp":1766820627290,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"111 Project of China","award":["B14010"],"award-info":[{"award-number":["B14010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Attacking a naval vessel with multiple missiles is an important way to improve the hit rate of missiles. Missile-borne radars need to complete detection and antijamming tasks to guide missiles, but communication between these radars is often difficult. In this paper, an optimization method based on multi-agent reinforcement learning is proposed for the collaborative detection and antijamming tasks of multiple radars against one naval vessel. We consider the collaborative radars as one player to make their confrontation with the naval vessel a two-person zero-sum game. With temporal constraints of the radar\u2019s and jammer\u2019s recognition and preparation interval, the game focuses on taking a favorable position at the end of the confrontation. It is assumed the total jamming capability of a shipborne jammer is constant and limited, and the shipborne jammer allocates the jamming capability in the radar\u2019s direction according to the radar threat assessment result and its probability of successful detection. The radars work collaboratively through prior centralized training and obtain a good performance by decentralized execution. The proposed method can make radars collaborate to detect the naval vessel, rather than only considering the detection result of each radar itself. Experimental results show that the proposed method in this paper is effective, improving the winning probability to 10% and 25% in the two-radar and four-radar scenarios, respectively.<\/jats:p>","DOI":"10.3390\/rs15112893","type":"journal-article","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T01:33:54Z","timestamp":1685669634000},"page":"2893","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An Optimization Method for Collaborative Radar Antijamming Based on Multi-Agent Reinforcement Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9219-0865","authenticated-orcid":false,"given":"Cheng","family":"Feng","sequence":"first","affiliation":[{"name":"Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Xiongjun","family":"Fu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing 100081, China"},{"name":"Tangshan Research Institute of BIT, Tangshan 063007, China"}]},{"given":"Ziyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Jian","family":"Dong","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Zhichun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Teng","family":"Pan","sequence":"additional","affiliation":[{"name":"Beijing Institute of Space Systems Engineering, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1109\/22.989948","article-title":"Electronic warfare systems","volume":"50","author":"Spezio","year":"2002","journal-title":"IEEE Trans. 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