{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T10:14:42Z","timestamp":1769768082739,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T00:00:00Z","timestamp":1605657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Data association is a crucial component of multiple target tracking, in which each measurement obtained by the sensor can be determined whether it belongs to the target. However, many methods reported in the literature may not be able to ensure the accuracy and low computational complexity during the association process, especially in the presence of dense clutters. In this paper, a novel data association method based on reinforcement learning (RL), i.e., the so-called RL-JPDA method, has been proposed for solving the aforementioned problem. In the presented method, the RL is leveraged to acquire available information of measurements. In addition, the motion characteristics of the targets are utilized to ensure the accuracy of the association results. Experiments are performed to compare the proposed method with the global nearest neighbor data association method, the joint probabilistic data association method, the fuzzy optimal membership data association method and the intuitionistic fuzzy joint probabilistic data association method. The results show that the proposed method yields a shorter execution time compared to other methods. Furthermore, it can obtain an effective and feasible estimation in the environment with dense clutters.<\/jats:p>","DOI":"10.3390\/s20226595","type":"journal-article","created":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T07:41:00Z","timestamp":1605685260000},"page":"6595","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Reinforcement Learning-Based Data Association for Multiple Target Tracking in Clutter"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9147-2614","authenticated-orcid":false,"given":"Chengzhi","family":"Qu","sequence":"first","affiliation":[{"name":"School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9890-8380","authenticated-orcid":false,"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5673-309X","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4134-901X","authenticated-orcid":false,"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518000, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.knosys.2015.04.018","article-title":"One global optimization method in network flow model for multiple object tracking","volume":"86","author":"He","year":"2015","journal-title":"Knowl. Based Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"112969","DOI":"10.1016\/j.eswa.2019.112969","article-title":"Modified smoothing data association for target tracking in clutter","volume":"141","author":"Memon","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.asoc.2005.11.005","article-title":"Information fusion in data association applications","volume":"6","author":"Chen","year":"2006","journal-title":"Appl. Soft Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.neucom.2019.06.003","article-title":"Multi-target tracking method based on improved firefly algorithm optimized particle filter","volume":"359","author":"Tian","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1725","DOI":"10.1109\/TAES.2018.2875555","article-title":"GP-PDA filter for extended target tracking with measurement origin uncertainty","volume":"55","author":"Guo","year":"2019","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1640","DOI":"10.1109\/TAES.2018.2874147","article-title":"A box particle filter method for tracking multiple extended objects","volume":"55","author":"Mihaylova","year":"2019","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2146","DOI":"10.1109\/TPAMI.2018.2849374","article-title":"On detection, data association and segmentation for multi-target tracking","volume":"41","author":"Yicong","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1109\/TAC.1973.1100421","article-title":"New results in optimizing surveillance system tracking and data correlation performance in dense multitarget environments","volume":"18","author":"Singer","year":"1973","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.ast.2012.02.017","article-title":"A new nearest-neighbor association approach based on fuzzy clustering","volume":"26","author":"Aziz","year":"2013","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bar-Shalom, Y., and Jaffer, A.G. (1972, January 13\u201315). Adaptive nonlinear filtering for tracking with measurements of uncertain origin. Proceedings of the 1972 IEEE Conference on Decision and Control and 11th Symposium on Adaptive Processes, New Orleans, LA, USA.","DOI":"10.1109\/CDC.1972.268994"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2797","DOI":"10.1109\/TSP.2006.874803","article-title":"A probabilistic nearest neighbor filter algorithm form validated measurements","volume":"54","author":"Song","year":"2006","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.inffus.2015.10.001","article-title":"Multi-Hypotheses Tracking using the Dempster-Shafer Theory. Application to ambiguous road context","volume":"29","author":"Gruyer","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/JSTSP.2013.2258322","article-title":"A multiple hypothesis tracker for multitarget tracking with multiple simultaneous measurements","volume":"7","author":"Sathyan","year":"2013","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Formann, T., Bar-Shalom, Y., and Scheffe, M. (1980, January 10\u201312). Multi-target tracking using joint probabilistic data association. Proceedings of the Conference on Decision and Control Including the Symposium on Adaptive Processes, Albuquerque, NM, USA.","DOI":"10.1109\/CDC.1980.271915"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1109\/TAC.1984.1103597","article-title":"Joint probabilistic data association for multitarget tracking with possibly unresolved measurements and maneuvers","volume":"29","author":"Chang","year":"1984","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1109\/TAES.2007.4285353","article-title":"Novel data association schemes for the probability hypothesis density filter","volume":"43","author":"Panta","year":"2007","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5702","DOI":"10.1109\/TSP.2019.2943234","article-title":"Trajectory PHD and CPHD Filters","volume":"67","author":"Svensson","year":"2019","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1109\/TSP.2013.2259822","article-title":"Labeled Random Finite Sets and Multi-Object Conjugate Priors","volume":"61","author":"Vo","year":"2013","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6177","DOI":"10.1109\/TSP.2018.2872856","article-title":"A Generalized Labeled Multi-Bernoulli Filter with Object Spawning","volume":"66","author":"Bryant","year":"2018","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Magnier, V., Gruyer, D., and Godelle, J. (2017, January 11\u201314). Multi-criteria Similarity Operator based on the Belief Theory:Management of Similarity, Dissimilarity, Conflict and Ambiguities. Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995878"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1109\/JSEN.2012.2213892","article-title":"Sensor Fusion and Target Tracking Using Evidential Data Association","volume":"13","author":"Dallil","year":"2013","journal-title":"IEEE Sens. J."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Magnier, V., and Gruyer, D. (2014, January 8\u201312). Dual Multi-Targets Tracking for Ambiguities\u2019 Identification and Solving. Proceedings of the 2014 IEEE Intelligent Vehicles Symposium, Dearborn, MI, USA.","DOI":"10.1109\/IVS.2014.6856512"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.sigpro.2013.10.011","article-title":"Intuitionistic fuzzy joint probabilistic data association filter and its application to multitarget tracking","volume":"96","author":"Liangqun","year":"2014","journal-title":"Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8074","DOI":"10.1109\/JSEN.2018.2863105","article-title":"Multi-sensor multi-target tracking using domain knowledge and clustering","volume":"18","author":"He","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.eswa.2017.01.059","article-title":"Soft and evolutionary computation based data association approaches for tracking multiple targets in the presence of ECM","volume":"77","author":"Gnane","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1078\/1434-8411-54100254","article-title":"Cheap Joint Probabilistic Data Association with Adaptive Neuro-Fuzzy Inference System State Filter for Tracking Multiple Targets in Cluttered Environment","volume":"58","author":"Turkmen","year":"2004","journal-title":"AEU Int. J. Electron. Commun."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1177\/0278364913495721","article-title":"Reinforcement learning in robotics: A survey","volume":"32","author":"Kober","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1109\/TSMCC.2012.2218595","article-title":"A survey of actor-critic reinforcement learning: Standard and natural policy gradients","volume":"42","author":"Grondman","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part C"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2042","DOI":"10.1109\/TNNLS.2017.2773458","article-title":"Optimal and autonomous control using reinforcement learning: A survey","volume":"29","author":"Kiumarsi","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1109\/TSMCA.2012.2227719","article-title":"A deterministic improved Q-learning for path planning of a mobile robot","volume":"43","author":"Konar","year":"2013","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"298","DOI":"10.3389\/fbioe.2020.00298","article-title":"Deep Reinforcement Learning for Data Association in Cell Tracking","volume":"8","author":"Wang","year":"2020","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"57814","DOI":"10.1109\/ACCESS.2018.2872751","article-title":"UGV navigation optimization aided by reinforcement learning-based path tracking","volume":"6","author":"Wei","year":"2018","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.robot.2018.05.016","article-title":"Adaptive low-level control of autonomous underwater vehicles using deep reinforcement learning","volume":"107","author":"Carlucho","year":"2018","journal-title":"Robot. Auton. Syst."},{"key":"ref_34","first-page":"513","article-title":"Improved integrated probabilistic data association algorithm based on amplitude information","volume":"37","author":"Li","year":"2015","journal-title":"Robot"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1109\/TAES.2012.6178056","article-title":"Track Quality Based Multitarget Tracking Approach for Global Nearest-Neighbor Association","volume":"48","author":"Sinha","year":"2012","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Aziz Ashraf, M. (2015, January 7\u201314). A new multitarget tracking approach based on a non-iterative fuzzy clustering means algorithm. Proceedings of the IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2015.7119236"},{"key":"ref_37","unstructured":"Gehly, S., Jones, B., and Axelrad, P. (2014, January 9\u201312). An AEGIS-CPHD Filter to Maintain Custody of GEO Space Objects with Limited Tracking Data. Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference, Maui, HI, USA."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1109\/JPROC.2003.823141","article-title":"Unscented filtering and nonlinear estimation","volume":"92","author":"Julier","year":"2004","journal-title":"Proc. IEEE"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/22\/6595\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:33:48Z","timestamp":1760178828000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/22\/6595"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,18]]},"references-count":38,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["s20226595"],"URL":"https:\/\/doi.org\/10.3390\/s20226595","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,18]]}}}