{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T09:27:12Z","timestamp":1774603632762,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T00:00:00Z","timestamp":1690502400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971433"],"award-info":[{"award-number":["61971433"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZR2020MF015"],"award-info":[{"award-number":["ZR2020MF015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["tsqn202211247"],"award-info":[{"award-number":["tsqn202211247"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["61971433"],"award-info":[{"award-number":["61971433"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2020MF015"],"award-info":[{"award-number":["ZR2020MF015"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["tsqn202211247"],"award-info":[{"award-number":["tsqn202211247"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010040","name":"Taishan Scholar Project of Shandong Province","doi-asserted-by":"publisher","award":["61971433"],"award-info":[{"award-number":["61971433"]}],"id":[{"id":"10.13039\/501100010040","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010040","name":"Taishan Scholar Project of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020MF015"],"award-info":[{"award-number":["ZR2020MF015"]}],"id":[{"id":"10.13039\/501100010040","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010040","name":"Taishan Scholar Project of Shandong Province","doi-asserted-by":"publisher","award":["tsqn202211247"],"award-info":[{"award-number":["tsqn202211247"]}],"id":[{"id":"10.13039\/501100010040","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Compared with traditional active detection radar, non-cooperative bistatic radar has a series of advantages, such as a low cost and low detectability. However, in real-life scenarios, it is limited by the non-cooperation of the radiation source and the bistatic geometric model, resulting in a low target signal-to-noise ratio (SNR) and unstable detection between frames in the radar scanning cycle. The traditional detect-before-track (DBT) method fails to exploit adequately the target information and is incapable of achieving consistent and effective tracking. Therefore, in this paper, we propose a two-stage track-before-detect (TBD) method based on deep learning. This method employs a low-threshold detection network to identify the target initially, followed by utilizing the model method to ascertain potential tracks. Subsequently, a diverse range of network structures are employed to extract and integrate position information, innovation score, and target structural information from the track in order to obtain the target track. Experimental results demonstrate the method\u2019s ability to achieve multi-target tracking in highly cluttered environments, where the higher the number of frames processed, the better the target tracking effect. Moreover, the method exhibits real-time processing capabilities. Hence, this method provides an effective solution for target tracking in non-cooperative bistatic radar systems.<\/jats:p>","DOI":"10.3390\/rs15153757","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T07:58:52Z","timestamp":1690531132000},"page":"3757","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Two-Stage Track-before-Detect Method for Non-Cooperative Bistatic Radar Based on Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Wei","family":"Xiong","sequence":"first","affiliation":[{"name":"Maritime Target Detection Research Group, Naval Aviation University, Yantai 264001, China"}]},{"given":"Yuan","family":"Lu","sequence":"additional","affiliation":[{"name":"Maritime Target Detection Research Group, Naval Aviation University, Yantai 264001, China"}]},{"given":"Jie","family":"Song","sequence":"additional","affiliation":[{"name":"Maritime Target Detection Research Group, Naval Aviation University, Yantai 264001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1040-1655","authenticated-orcid":false,"given":"Xiaolong","family":"Chen","sequence":"additional","affiliation":[{"name":"Maritime Target Detection Research Group, Naval Aviation University, Yantai 264001, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,28]]},"reference":[{"key":"ref_1","first-page":"496","article-title":"Analysis of key technologies of non-cooperative radar emitter target detection system","volume":"35","author":"Bao","year":"2020","journal-title":"J. Radio Wave Sci."},{"key":"ref_2","first-page":"285","article-title":"Bistatic detection system based on geosynchronous synthetic aperture radar: Concept and potential","volume":"29","author":"Mao","year":"2013","journal-title":"Signal Process."},{"key":"ref_3","first-page":"54","article-title":"Complex envelope estimation technology of direct wave pulse based on improved CMA + MMA","volume":"32","author":"Ge","year":"2010","journal-title":"Mod. Radar"},{"key":"ref_4","unstructured":"Xi, Z. (2020). Research on Key Technologies of Clutter Interference Suppression and Target Tracking for Non-Cooperative Bistatic Radar. [Ph.D. Thesis, National University of Defense Technology]."},{"key":"ref_5","unstructured":"Yi, W. (2012). Research on multi-target tracking algorithm based on track-before-detect technology. [Ph.D. Thesis, University of Electronic Science and Technology of China]."},{"key":"ref_6","unstructured":"Fu, L.Z., Wang, B., Cao, Y., and Yi, W. (2022, January 19\u201320). Multi-station anti-radiation radar target tracking algorithm based on PF-TBD. Proceedings of the Tenth China Aviation Association Youth Science and Technology Forum Papers, Nanchang, China."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2458","DOI":"10.1109\/TAES.2012.6237603","article-title":"Recursive Bayesian filtering for multitarget track-before-detect in passive radars","volume":"48","author":"Lehmann","year":"2012","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Santi, F., Pastina, D., and Bucciarelli, M. (2020). Experimental demonstration of ship target detection in GNSS-based passive radar combining target motion compensation and track-before-detect strategies. Sensors, 20.","DOI":"10.3390\/s20030599"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Knoedler, B., Steffes, C., and Koch, W. (2020, January 21\u201325). Detecting and tracking a small UAV in GSM passive radar using track-before-detect. Proceedings of the 2020 IEEE Radar Conference (RadarConf20), Florence, Italy.","DOI":"10.1109\/RadarConf2043947.2020.9266673"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3911956","DOI":"10.1155\/2021\/3911956","article-title":"Track-Before-Detect Procedures in AM Radio-Based Passive Radar","volume":"2021","author":"Li","year":"2021","journal-title":"Int. J. Antennas Propag."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Testolin, A., and Diamant, R. (2020). Combining denoising autoencoders and dynamic programming for acoustic detection and tracking of underwater moving targets. Sensors, 20.","DOI":"10.3390\/s20102945"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Carr, C., Dang, B., and Metcalf, J. (2021, January 7\u201314). RADGAN: Applying adversarial machine learning to track-before-detect radar. Proceedings of the 2021 IEEE Radar Conference (RadarConf21), Atlanta, GA, USA.","DOI":"10.1109\/RadarConf2147009.2021.9455179"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wen, L., Zhong, C., Huang, X., and Ding, J. (2019, January 26\u201329). Sea clutter suppression based on selective reconstruction of features. Proceedings of the 2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Xiamen, China.","DOI":"10.1109\/APSAR46974.2019.9048548"},{"key":"ref_14","first-page":"5107116","article-title":"Multiframe detection of sea-surface small target using deep convolutional neural network","volume":"60","author":"Wen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhu, C., Deng, J., Long, X., Zhang, W., and Yi, W. (2022, January 21\u201324). DBU-Net Based Robust Target Detection for Multi-Frame Track-Before-Detect Method. Proceedings of the 2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS), Hanoi, Vietnam.","DOI":"10.1109\/ICCAIS56082.2022.9990429"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2608","DOI":"10.1109\/TSP.2013.2251338","article-title":"A novel dynamic programming algorithm for track-before-detect in radar systems","volume":"61","author":"Grossi","year":"2013","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Song, J., Xiong, W., Chen, X., and Lu, Y. (2022). Experimental study of maritime moving target detection using hitchhiking bistatic radar. Remote Sens., 14.","DOI":"10.3390\/rs14153611"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pan, M., Chen, J., Wang, S., and Dong, Z. (2019, January 19\u201321). A novel approach for marine small target detection based on deep learning. Proceedings of the 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), Wuxi, China.","DOI":"10.1109\/SIPROCESS.2019.8868862"},{"key":"ref_19","unstructured":"Shao, Y.E. (2022). Multi-Static Passive Radar Target Location and Tracking Based on Navigation Satellite. [Master\u2019s Thesis, Xi\u2019an University of Electronic Science and Technology]."},{"key":"ref_20","unstructured":"Chen, X. (2021). Research on Improved Radar Multi-Target Track Initiation Algorithm. [Master\u2019s Thesis, Dalian Maritime University]."},{"key":"ref_21","unstructured":"Mayor, M.A., and Carroll, R.L. (1987, January 10\u201312). A multi-target track initiation algorithm. Proceedings of the 1987 American Control Conference, Minneapolis, MN, USA."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1109\/TAES.1981.309044","article-title":"Track initiation of occasionally unresolved radar targets","volume":"AES-17","author":"Trunk","year":"1981","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_23","first-page":"37","article-title":"Basic algorithm for trajectory initiation of ballistic target based on grid clustering","volume":"43","author":"Zhao","year":"2018","journal-title":"Fire Control. Command. Control."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Smith, M. (1980, January 10\u201312). Feature space transform for multitarget detection. Proceedings of the 1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, Albuquerque, NM, USA.","DOI":"10.1109\/CDC.1980.271920"},{"key":"ref_25","first-page":"552","article-title":"Trajectory initiation based on ant colony similarity weighted Hough transform","volume":"29","author":"Li","year":"2016","journal-title":"J. Sens. Technol."},{"key":"ref_26","first-page":"1269","article-title":"Trajectory Initiation Method Based on Spatio-Temporal Characteristics of Radar Measurements","volume":"20","author":"Shen","year":"2022","journal-title":"J. Terahertz Sci. Electron. Inf."},{"key":"ref_27","unstructured":"He, Y., Xiu, J.J., and Zhang, J.W. (2009). Radar Data Processing and Application, Electronic Industry Press. [2nd ed.]."},{"key":"ref_28","first-page":"32","article-title":"IMM-Singer Model Based Tracking Algorithm for Maneuvering Targets","volume":"37","author":"Tan","year":"2012","journal-title":"Fire Control. Command. Control."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1109\/TAES.1970.310128","article-title":"Estimating optimal tracking filter performance for manned maneuvering targets","volume":"AES-6","author":"Singer","year":"1970","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_30","unstructured":"Wang, J., Yi, W., and Kong, L. (2016, January 5\u20138). Improved DP-TBD methods based on multiple hypothesis testing for target early detection. Proceedings of the 2016 19th International Conference on Information Fusion (FUSION), Heidelberg, Germany."},{"key":"ref_31","unstructured":"Raghu, M., Poole, B., Kleinberg, J., Ganguli, S., and Sohl-Dickstein, J. (2017, January 6\u201311). On the expressive power of deep neural networks. Proceedings of the International Conference on Machine Learning, Sydney, NSW, Australia."},{"key":"ref_32","first-page":"1","article-title":"Analysis of JPEG steganography based on deep extraction of steganographic noise","volume":"50","author":"Fan","year":"2022","journal-title":"J. Xidian Univ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/0893-6080(91)90009-T","article-title":"Approximation capabilities of multilayer feedforward networks","volume":"4","author":"Hornik","year":"1991","journal-title":"Neural Netw."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/0920-5489(94)90017-5","article-title":"Advanced supervised learning in multi-layer perceptrons\u2014From backpropagation to adaptive learning algorithms","volume":"16","author":"Riedmiller","year":"1994","journal-title":"Comput. Stand. Interfaces"},{"key":"ref_35","unstructured":"Zhang, W., Tanida, J., Itoh, K., and Ichioka, Y. (,  1988). Shift-invariant pattern recognition neural network and its optical architecture. Proceedings of the Annual Conference of the Japan Society of Applied Physics, Canada."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mikolov, T., Kombrink, S., Burget, L., \u010cernock\u00fd, J., and Khudanpur, S. (2011, January 22\u201327). Extensions of recurrent neural network language model. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic.","DOI":"10.1109\/ICASSP.2011.5947611"},{"key":"ref_37","unstructured":"Luo, Z. (2019). Research on track-before-detect and fusion technology of weak target. [Ph.D. Thesis, Xi\u2019an University of Electronic Science and Technology]."},{"key":"ref_38","first-page":"490","article-title":"Research on multi-frame track- before-detect algorithm for netted radar","volume":"8","author":"Wang","year":"2019","journal-title":"J. Radars"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1214\/aos\/1176348768","article-title":"Variable kernel density estimation","volume":"20","author":"Terrell","year":"1992","journal-title":"Ann. Stat."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/7.250410","article-title":"Search radar detection and track with the Hough transform. I. system concept","volume":"30","author":"Carlson","year":"1994","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_41","unstructured":"Ding, L.F. (2009). Radar Principles, National Defense Industry Press. [3rd ed.]."},{"key":"ref_42","first-page":"570","article-title":"Track-before-detect algorithm based on parallel-line-coordinate transformation","volume":"43","author":"Bo","year":"2022","journal-title":"Acta Aeronaut. Astronaut. Sin."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3757\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:21:32Z","timestamp":1760127692000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3757"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,28]]},"references-count":42,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15153757"],"URL":"https:\/\/doi.org\/10.3390\/rs15153757","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,28]]}}}