{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:21:35Z","timestamp":1760149295949,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,13]],"date-time":"2023-07-13T00:00:00Z","timestamp":1689206400000},"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":["82101969","2022QFY01-16","2022GY-242","2021ZDYF-SF-0020"],"award-info":[{"award-number":["82101969","2022QFY01-16","2022GY-242","2021ZDYF-SF-0020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Regional Innovation Capability Guidance Program Project of Shaan xi Province","award":["82101969","2022QFY01-16","2022GY-242","2021ZDYF-SF-0020"],"award-info":[{"award-number":["82101969","2022QFY01-16","2022GY-242","2021ZDYF-SF-0020"]}]},{"name":"Key Research and Development Project of Shaan xi Province","award":["82101969","2022QFY01-16","2022GY-242","2021ZDYF-SF-0020"],"award-info":[{"award-number":["82101969","2022QFY01-16","2022GY-242","2021ZDYF-SF-0020"]}]},{"name":"Science and Technology Plan Project of Xian Yang City","award":["82101969","2022QFY01-16","2022GY-242","2021ZDYF-SF-0020"],"award-info":[{"award-number":["82101969","2022QFY01-16","2022GY-242","2021ZDYF-SF-0020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The key issue of multiple extended target tracking is to differentiate the origins of the measurements. The association of measurements with the possible origins within the target\u2019s extent is difficult, especially for occlusions or detection blind zones, which cause intermittent measurements. To solve this problem, a hierarchical network-based tracklet data association algorithm (ET-HT) is proposed. At the low association level, a min-cost network flow model based on the divided measurement sets is built to extract the possible tracklets. At the high association level, these tracklets are further associated with the final trajectories. The association is formulated as an integral programming problem for finding the maximum a posterior probability in the network flow model based on the tracklets. Moreover, the state of the extended target is calculated using the in-coordinate interval Kalman smoother. Simulation and experimental results show the superiority of the proposed ET-HT algorithm over JPDA- and RFS-based methods when measurements are intermittently unavailable.<\/jats:p>","DOI":"10.3390\/s23146372","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:49:30Z","timestamp":1689295770000},"page":"6372","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hierarchical Network-Based Tracklets Data Association for Multiple Extended Target Tracking with Intermittent Measurements"],"prefix":"10.3390","volume":"23","author":[{"given":"Kaiyi","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiguo","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1077-1656","authenticated-orcid":false,"given":"Tianli","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,13]]},"reference":[{"key":"ref_1","unstructured":"Mahler, R.P.S. (2014). Advances in Statistical Multisource-Multitarget Information Fusion, Artech House."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2973","DOI":"10.1109\/TAES.2016.130346","article-title":"Tracking of extended object or target group using random matrix: New model and approach","volume":"52","author":"Lan","year":"2016","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"65","DOI":"10.2478\/v10177-011-0009-8","article-title":"Multisensor Tracking of Marine Targets\u2014Decentralized Fusion of Kalman and Neural Filters","volume":"57","author":"Stateczny","year":"2011","journal-title":"Int. J. Electron. Telecommun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1109\/TSP.2006.889470","article-title":"A Bayesian Approach to Multiple Target Detection and Tracking","volume":"55","author":"Morelande","year":"2007","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1109\/JOE.2015.2503499","article-title":"Joint Probabilistic Data Association Tracker for Extended Target Tracking Applied to X-Band Marine Radar Data","volume":"41","author":"Vivone","year":"2016","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1109\/JSTSP.2013.2256772","article-title":"A Multiple-Detection Joint Probabilistic Data Association Filter","volume":"7","author":"Habtemariam","year":"2013","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_7","first-page":"175","article-title":"Multi- detection joint integrated probabilistic data association using random matrices with applications to radar-based multi object tracking","volume":"12","author":"Schuster","year":"2013","journal-title":"IEEE J. Adv. Inf. Fusion"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.dsp.2017.10.020","article-title":"Iterative joint integrated probabilistic data association filter for multiple-detection multiple-target tracking","volume":"72","author":"Xie","year":"2018","journal-title":"Digit. Signal Process."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, S., Wolf, L.M., and Baum, M. (2020, January 6\u20139). Marginal Association Probabilities for Multiple Extended Objects without Enumeration of Measurement Partitions. Proceedings of the 2020 IEEE 23rd International Conference on Information Fusion, Rustenburg, South Africa.","DOI":"10.23919\/FUSION45008.2020.9190500"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.sigpro.2018.04.003","article-title":"Modified Gaussian inverse Wishart PHD filter for tracking multiple non-ellipsoidal extended targets","volume":"150","author":"Li","year":"2018","journal-title":"Signal Process."},{"key":"ref_11","unstructured":"Peng, M.S., Linares, R., and Bageshwar, V.L. (2019, January 10\u201312). Extended Target Tracking and Shape Estimation via Random Finite Sets. Proceedings of the 2019 American Control Conference, Milwaukee, WI, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"57","DOI":"10.21629\/JSEE.2019.01.06","article-title":"Labeled box-particle CPHD filter for multiple extended targets tracking","volume":"30","author":"Zhibin","year":"2019","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"102669","DOI":"10.1016\/j.dsp.2020.102669","article-title":"Improved generalized labeled multi-Bernoulli filter for non-ellipsoidal extended targets or group targets tracking based on random sub-matrices","volume":"99","author":"Liang","year":"2020","journal-title":"Digit. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/TIV.2017.2788184","article-title":"Likelihood-Based Data Association for Extended Object Tracking Using Sampling Methods","volume":"3","author":"Granstrom","year":"2018","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.ijleo.2018.12.125","article-title":"An improved probability hypothesis density filter for multi-target tracking","volume":"182","author":"Zhang","year":"2019","journal-title":"Optik"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.1049\/iet-rsn.2018.5273","article-title":"Data association for extended target tracking by BP","volume":"12","author":"Su","year":"2018","journal-title":"IET Radar Sonar Navig."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2212","DOI":"10.1016\/j.cja.2020.01.004","article-title":"Loopy belief propagation based data association for extended target tracking","volume":"33","author":"Su","year":"2020","journal-title":"Chin. J. Aeronaut."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.automatica.2017.12.004","article-title":"Structure modeling and estimation of multiple resolvable group targets via graph theory and multi-Bernoulli filter","volume":"89","author":"Liu","year":"2018","journal-title":"Automatica"},{"key":"ref_19","unstructured":"Yazdian, D.M., and Azimifar, Z. (2015, January 10\u201314). An improvement on GM-PHD filter for target tracking in presence of subsequent miss-detection. Proceedings of the 2015 23rd Iranian Conference on Electrical Engineering, Tehran, Iran."},{"key":"ref_20","first-page":"180","article-title":"Multiple-model multiple hypothesis probability hypothesis density filter with blind zone","volume":"27","author":"Ma","year":"2017","journal-title":"Int. J. Ind. Syst. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2912","DOI":"10.1109\/TSP.2020.2988635","article-title":"Tracking multiple maneuvering targets hidden in the DBZ based on the MM-GLMB filter","volume":"68","author":"Wu","year":"2020","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4164","DOI":"10.1109\/TII.2019.2898992","article-title":"Network flow labeling for extended target tracking PHD filters","volume":"15","author":"Yang","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1016\/j.automatica.2011.09.015","article-title":"Mean Square Stability for Kalman Filtering with Markovian Packet Losses","volume":"47","author":"You","year":"2011","journal-title":"Automatica"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103678","DOI":"10.1109\/ACCESS.2019.2931470","article-title":"Cubature information Gaussian mixture probability hypothesis density approach for multi extended target tracking","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1049\/iet-rsn.2018.5598","article-title":"HRRP multi-target recognition in a beam using prior-independent DBSCAN clustering algorithm","volume":"13","author":"Guo","year":"2019","journal-title":"IET Radar Sonar Navig."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"481719","DOI":"10.1155\/2014\/481719","article-title":"Multiple Object Tracking Using the Shortest Path Faster Association Algorithm","volume":"2014","author":"Xi","year":"2014","journal-title":"Sci. World J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhao, L., and He, Z. (2012, January 25\u201327). An in-coordinate interval adaptive Kalman filtering algorithm for INS\/GPS\/SMNS. Proceedings of the IEEE 10th International Conference on Industrial Informatics, Beijing, China.","DOI":"10.1109\/INDIN.2012.6301054"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.automatica.2017.08.011","article-title":"Generalized Kalman smoothing: Modeling and algorithms","volume":"86","author":"Aravkin","year":"2017","journal-title":"Automatica"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.sigpro.2014.01.034","article-title":"Collaborative penalized Gaussian mixture PHD tracker for close target tracking","volume":"102","author":"Wang","year":"2014","journal-title":"Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3447","DOI":"10.1109\/TSP.2008.920469","article-title":"A consistent metric for performance evaluation of multi-object filters","volume":"56","author":"Schuhmacher","year":"2008","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ma, T., Gao, S., Chen, C., and Song, X. (2018). Multitarget Tracking Algorithm Based on Adaptive Network Graph Segmentation in the Presence of Measurement Origin Uncertainty. Sensors, 18.","DOI":"10.3390\/s18113791"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","article-title":"Object detection with discriminatively trained part-based models","volume":"32","author":"Felzenszwalb","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6372\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:12:19Z","timestamp":1760127139000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6372"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,13]]},"references-count":32,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23146372"],"URL":"https:\/\/doi.org\/10.3390\/s23146372","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,7,13]]}}}