{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T10:43:10Z","timestamp":1783248190597,"version":"3.54.6"},"reference-count":40,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,8,3]],"date-time":"2021-08-03T00:00:00Z","timestamp":1627948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Nation Natural Science Foundation of China, 647 the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 project)","award":["No.B18039"],"award-info":[{"award-number":["No.B18039"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To combat main lobe jamming, preventive measures can be applied to radar in advance based on the concept of active antagonism, and efficient antijamming strategies can be designed through reinforcement learning. However, uncertainties in the radar and the jammer, which will result in a mismatch between the test and training environments, are not considered. Therefore, a robust antijamming strategy design method is proposed in this paper, in which frequency-agile radar and a main lobe jammer are considered. This problem is first formulated under the framework of Wasserstein robust reinforcement learning. Then, the method of imitation learning-based jamming strategy parameterization is presented to express the given jamming strategy mathematically. To reduce the number of parameters that require optimization, a perturbation method inspired by NoisyNet is also proposed. Finally, robust antijamming strategies are designed by incorporating jamming strategy parameterization and jamming strategy perturbation into Wasserstein robust reinforcement learning. The simulation results show that the robust antijamming strategy leads to improved radar performance compared with the nonrobust antijamming strategy when uncertainties exist in the radar and the jammer.<\/jats:p>","DOI":"10.3390\/rs13153043","type":"journal-article","created":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T02:16:07Z","timestamp":1628043367000},"page":"3043","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Robust Antijamming Strategy Design for Frequency-Agile Radar against Main Lobe Jamming"],"prefix":"10.3390","volume":"13","author":[{"given":"Kang","family":"Li","sequence":"first","affiliation":[{"name":"The National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Jiu","sequence":"additional","affiliation":[{"name":"The National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwei","family":"Liu","sequence":"additional","affiliation":[{"name":"The National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenqiang","family":"Pu","sequence":"additional","affiliation":[{"name":"Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen 518172, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,3]]},"reference":[{"key":"ref_1","unstructured":"Su, B., Wang, Y., and Zhou, L. 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