{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T22:22:45Z","timestamp":1767046965779,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,1,26]],"date-time":"2024-01-26T00:00:00Z","timestamp":1706227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,1,26]]},"DOI":"10.1145\/3640824.3640834","type":"proceedings-article","created":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T12:05:28Z","timestamp":1709899528000},"page":"66-72","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Radar sequence HRRP target recognition based on DRSN-LSTM"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6444-5832","authenticated-orcid":false,"given":"Jianguo","family":"Yin","sequence":"first","affiliation":[{"name":"Air Force Early Warning Academy, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7929-2174","authenticated-orcid":false,"given":"Sheng","family":"Wen","sequence":"additional","affiliation":[{"name":"Air Force Early Warning Academy, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1330-362X","authenticated-orcid":false,"given":"Chenhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Unit 95866 of PLA, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7274-8752","authenticated-orcid":false,"given":"Meng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Unit 95866 of PLA, China"}]}],"member":"320","published-online":{"date-parts":[[2024,3,8]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_3_2_1_1_1","DOI":"10.1109\/ECNCT59757.2023.10280852"},{"key":"e_1_3_2_1_2_1","first-page":"257","volume-title":"SPIE","author":"Yue Z","year":"2022","unstructured":"Yue Z, Lu J, Wan L. Radar HRRP recognition of ship targets based on R-MLPs[C]\/\/International Conference on Neural Networks, Information, and Communication Engineering (NNICE). SPIE, 2022, 12258: 257-263."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_3_1","DOI":"10.1109\/LGRS.2023.3279992"},{"doi-asserted-by":"crossref","unstructured":"Liu L Su M Liu J Support Vector Machine for HRRP Recognition based on Bald Eagle Search Optimization[C]\/\/Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering. 2022: 727-731.","key":"e_1_3_2_1_4_1","DOI":"10.1145\/3573428.3573557"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_5_1","DOI":"10.1016\/j.sigpro.2021.108391"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_6_1","DOI":"10.1109\/TAES.2019.2925472"},{"key":"e_1_3_2_1_7_1","first-page":"397","volume-title":"5th EAI International Conference on IoT as a Service(IoTaaS 2019","author":"Li Jieqi","year":"2020","unstructured":"Li Jieqi, Li Shaojie, and Liu Qi, A Novel Algorithm for HRRP Target Recognition Based on CNN[C]. 5th EAI International Conference on IoT as a Service(IoTaaS 2019). Xi'an, China, Nov.2020:397-404."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_8_1","DOI":"10.1109\/RADAR.2016.7485271"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_9_1","DOI":"10.1109\/ACCESS.2019"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_10_1","DOI":"10.1007\/978-3-030-67514-1_56"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_11_1","DOI":"10.1016\/j.sigpro.2018.09.041"},{"key":"e_1_3_2_1_12_1","first-page":"346","volume-title":"2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML 2022","author":"Wang Xiaodan","year":"2022","unstructured":"Wang Xiaodan, Wang Peng, and Song Yafei, Recognition of HRRP sequence based on TCN with attention and elastic net regularization[C]. 2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML 2022). Virtual, Online, China, Oct.2022:346-351."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_13_1","DOI":"10.1109\/TGRS.2021.3055061"},{"key":"e_1_3_2_1_14_1","volume-title":"Deep Residual Learning for Image Recognition[C]\/\/IEEE Conference on Computer Vision and Pattern Recognition(CVPR)","author":"He X.","year":"2016","unstructured":"K. He, X. Zhang, S. Ren and J. Sun. Deep Residual Learning for Image Recognition[C]\/\/IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Las Vegas: IEEE, 2016: 770-778."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_15_1","DOI":"10.1109\/TII.2019.2943898"},{"key":"e_1_3_2_1_16_1","first-page":"1129","article-title":"Radar air target recognition based on deep residual shrinkage network [J\/OL]. Systems Engineering and Electronics: 1-8[2023-12-19]. http:\/\/kns.cnki.net\/kcms\/detail\/11.2422.","author":"Yin Jianguo","year":"2023","unstructured":"Yin Jianguo, Sheng Wen, Jiang Wei. Radar air target recognition based on deep residual shrinkage network [J\/OL]. Systems Engineering and Electronics: 1-8[2023-12-19]. http:\/\/kns.cnki.net\/kcms\/detail\/11.2422.TN. 20231129.1127.007.html.","journal-title":"TN."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_17_1","DOI":"10.1109\/CIE-Radar.2011.6159624"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_18_1","DOI":"10.1109\/tsp.2011.2141664"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_19_1","DOI":"10.1109\/TSP.2021.3065847"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_20_1","DOI":"10.1109\/ACCESS.2020.2969450"},{"key":"e_1_3_2_1_21_1","first-page":"243","volume-title":"International Conference on Intelligent Science and Big Data Engineering","author":"Mengqi CHEN Bo","year":"2018","unstructured":"SHEN Mengqi, CHEN Bo. Radar HRRP Target Recognition with Recurrent Convolutional Neural Networks[C]. International Conference on Intelligent Science and Big Data Engineering. Lanzhou, China, Aug.2018:243-251."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_22_1","DOI":"10.1051\/matecconf\/201817303055"},{"key":"e_1_3_2_1_23_1","first-page":"1047","article-title":"HRRP target recognition based on soft-boundary deep SVDD with LSTM[C]\/\/2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"2021","author":"Sun L","unstructured":"Sun L, Liu J, Liu Y, HRRP target recognition based on soft-boundary deep SVDD with LSTM[C]\/\/2021 International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE, 2021: 1047-1052.","journal-title":"IEEE"},{"volume-title":"IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE","author":"Yonggang L","unstructured":"Yonggang L, Libing G, Yi W, A Method of Radar HRRP Aircraft Type Identification and Rejection Based on LSTM[C]\/\/2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2023: 1115-1118.","key":"e_1_3_2_1_24_1"},{"issue":"10","key":"e_1_3_2_1_25_1","first-page":"2775","article-title":"Radar HRRP sequence target recognition method of attention mechanism based stacked LSTM network[J]","volume":"43","author":"Yifan ZHANG","year":"2021","unstructured":"ZHANG Yifan, ZHANG Shuanghui, LIU Yongxiang. Radar HRRP sequence target recognition method of attention mechanism based stacked LSTM network[J]. Systems Engineering and Electronics, 2021, 43(10): 2775-2781.","journal-title":"Systems Engineering and Electronics"},{"doi-asserted-by":"crossref","unstructured":"Jithesh V M Justin Sagayaraj and K. G Srinivasa. LSTM recurrent neural networks for high resolution range profile based radar target classification[C]. 2017 3rd International conference on computational intelligence & communication technology (CICT). Ghaziabad India 2017:1-6.","key":"e_1_3_2_1_26_1","DOI":"10.1109\/CIACT.2017.7977298"}],"event":{"acronym":"CCEAI 2024","name":"CCEAI 2024: 2024 8th International Conference on Control Engineering and Artificial Intelligence","location":"Shanghai China"},"container-title":["2024 8th International Conference on Control Engineering and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3640824.3640834","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3640824.3640834","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T16:47:21Z","timestamp":1756486041000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3640824.3640834"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,26]]},"references-count":26,"alternative-id":["10.1145\/3640824.3640834","10.1145\/3640824"],"URL":"https:\/\/doi.org\/10.1145\/3640824.3640834","relation":{},"subject":[],"published":{"date-parts":[[2024,1,26]]},"assertion":[{"value":"2024-03-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}