{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T01:48:42Z","timestamp":1768873722196,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T00:00:00Z","timestamp":1672012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62071493"],"award-info":[{"award-number":["62071493"]}]},{"name":"National Natural Science Foundation of China","award":["61831010"],"award-info":[{"award-number":["61831010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to high false alarm rate and low positioning accuracy of compact high-frequency surface wave radar in moving vessel detection, false plot-to-track association often occurs during moving vessel tracking, thus leading to track fragmentation and false tracking. In order to address this problem, a plot quality evaluation method is proposed and applied to plot-to-track association. Firstly, the differences in spatial correlation of echo spectrum amplitudes and position among moving vessels, clutters, and noise on a range-Doppler map are analyzed, and a plot quality index integrating multi-directional gradient, local variance, and plot position probability is developed. Then, the plots labeled as low quality are removed to reduce both the negative impact of false alarms on plot-to-track association and the computational burden. Eventually, both plot quality index and kinematic parameters are used to calculate the association cost and determine the plot-track pairs during the plot-to-track association procedure. Experimental results with field data demonstrate that the proposed plot quality index can effectively distinguish moving vessel and other plots. Compared with both the nearest neighbor data association method and the joint probability data association method, the association accuracy of the proposed method is greatly improved and, thus, the tracking continuity is enhanced in dense clutter scenarios.<\/jats:p>","DOI":"10.3390\/rs15010138","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T02:53:11Z","timestamp":1672109591000},"page":"138","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Plot Quality Aided Plot-to-Track Association in Dense Clutter for Compact High-Frequency Surface Wave Radar"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8381-6483","authenticated-orcid":false,"given":"Weifeng","family":"Sun","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Xiaotong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3264-8560","authenticated-orcid":false,"given":"Yonggang","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Yongshou","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9622-5041","authenticated-orcid":false,"given":"Weimin","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John\u2019s, NL A1B 3X5, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MAES.2018.170023","article-title":"Target Monitoring Using Small-aperture Compact High-frequency Surface Wave Radar","volume":"33","author":"Ji","year":"2018","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sun, W., Pang, Z., Huang, W., Ma, P., Ji, Y., Dai, Y., and Li, X. (2022). A Multi-Stage Vessel Tracklet Association Method for Compact High-Frequency Surface Wave Radar. Remote Sens., 14.","DOI":"10.3390\/rs14071601"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Huang, W., and Gill, E.W. (2021). Ocean Remote Sensing Technologies\u2014High-Frequency, Marine and GNSS-Based Radar, SciTech Publishing.","DOI":"10.1049\/SBRA537E"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sun, W., Ji, M., Huang, W., Ji, Y., and Dai, Y. (2020). Vessel Tracking Using Bistatic Compact HFSWR. Remote Sens., 12.","DOI":"10.3390\/rs12081266"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1109\/TGRS.2019.2943065","article-title":"A Vessel Azimuth and Course Joint Re-Estimation Method for Compact HFSWR","volume":"58","author":"Sun","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4427","DOI":"10.1109\/JSTARS.2021.3071625","article-title":"Vessel Velocity Estimation and Tracking from Doppler Echoes of T\/R-R Composite Compact HFSWR","volume":"14","author":"Sun","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","first-page":"3505005","article-title":"Improved CFAR Detection and Direction Finding on Time\u2013Frequency Plane with High-Frequency Radar","volume":"19","author":"Yang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Earle, M.D., and Malahoff, A. (1979). A Compact Transportable HF Radar System for Directional Coastal Wave Field Measurements. Ocean Wave Climate. Marine Science, Springer.","DOI":"10.1007\/978-1-4684-3399-9"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Su, R., Tang, J., Yuan, J., and Bi, Y. (2021, January 6\u20138). Nearest Neighbor Data Association Algorithm Based on Robust Kalman Filtering. Proceedings of the 2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), Nanjing, China.","DOI":"10.1109\/ISCEIC53685.2021.00044"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/TSP.2009.2030640","article-title":"Bayesian Multi-Object Filtering with Amplitude Feature Likelihood for Unknown Object SNR","volume":"58","author":"Clark","year":"2010","journal-title":"IEEE T. Signal. Proces."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"30385","DOI":"10.3390\/s151229804","article-title":"Multi-Target Tracking Based on Multi-Bernoulli Filter with Amplitude for Unknown Clutter Rate","volume":"15","author":"Yuan","year":"2015","journal-title":"Sensors"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, C., Sun, J., Lei, P., and Qi, Y. (2018). \u03b4-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells. Sensors, 18.","DOI":"10.3390\/s18041153"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, L., You, W., Wu, Q.M.J., Qi, S., and Ji, Y. (2018). Deep Learning-Based Automatic Clutter\/Interference Detection for HFSWR. Remote Sens., 10.","DOI":"10.3390\/rs10101517"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, L., Wang, S., Zhao, T., Wang, Y., and Li, Y. (2016, January 10\u201313). Radar HRRP Target Recognition Using Scattering Centers Fuzzy Matching. Proceedings of the 2016 CIE International Conference on Radar (RADAR), Guangzhou, China.","DOI":"10.1109\/RADAR.2016.8059195"},{"key":"ref_15","first-page":"3506305","article-title":"Fast Ship Detection with Spatial-Frequency Analysis and ANOVA-Based Feature Fusion","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1049\/iet-rsn.2017.0481","article-title":"Linear Multitarget Integrated Probabilistic Data Association for Multiple Detection Target Tracking","volume":"12","author":"Huang","year":"2018","journal-title":"IET Radar. Sonar. Nav."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1109\/TAES.2020.3018899","article-title":"A Sequential Target Existence Statistic for Joint Probabilistic Data Association","volume":"57","author":"Ainsleigh","year":"2021","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/7.481251","article-title":"Distributed CFAR Detection in Homogeneous and Nonhomogeneous Backgrounds","volume":"32","author":"Uner","year":"1996","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1109\/TAES.2020.3017319","article-title":"Rank Sum Nonparametric CFAR Detector in Nonhomogeneous Background","volume":"57","author":"Meng","year":"2021","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/LGRS.2018.2807405","article-title":"A Support Vector Regression-Based Method for Target Direction of Arrival Estimation From HF Radar Data","volume":"15","author":"Wang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, J., Yang, Q., Zhang, X., Ji, X., and Xiao, D. (2022). Space-Time Adaptive Processing Clutter-Suppression Algorithm Based on Beam Reshaping for High-Frequency Surface Wave Radar. Remote Sens., 14.","DOI":"10.3390\/rs14122935"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1109\/LGRS.2017.2691741","article-title":"Compact HF Surface Wave Radar Data Generating Simulator for Ship Detection and Tracking","volume":"14","author":"Park","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"157437","DOI":"10.1109\/ACCESS.2019.2944950","article-title":"Comparative Study on Chaos Identification of Ionospheric Clutter From HFSWR","volume":"7","author":"Lyu","year":"2019","journal-title":"IEEE Access."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/LGRS.2019.2920092","article-title":"Radio Frequency Interference Suppression for HF Surface Wave Radar Using CEMD and Temporal Windowing Methods","volume":"17","author":"Nazari","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5581","DOI":"10.1109\/JSTARS.2021.3082044","article-title":"Target Detection in Clutter\/Interference Regions Based on Deep Feature Fusion for HFSWR","volume":"14","author":"Wu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1109\/TAES.2014.120563","article-title":"Decorrelated Unbiased Converted Measurement Kalman Filter","volume":"50","author":"Bordonaro","year":"2014","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1109\/TGRS.2020.2997356","article-title":"Quality Control of Compact High-Frequency Radar-Retrieved Wave Data","volume":"59","author":"Tian","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1109\/LGRS.2017.2673806","article-title":"Automatic Detection of Ship Targets Based on Wavelet Transform for HF Surface Wavelet Radar","volume":"14","author":"Li","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, Y.R., Chuang, L.Z.H., and Chung, Y.J. (2018, January 28\u201331). Automated Peak Detection in Doppler Spectra of HF Surface Wave Radar. Proceedings of the 2018 OCEANS\u2014MTS\/IEEE Kobe Techno-Oceans (OTO), Kobe, Japan.","DOI":"10.1109\/OCEANSKOBE.2018.8559286"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/LGRS.2020.2967387","article-title":"Ship Detection and Direction Finding Based on Time-Frequency Analysis for Compact HF Radar","volume":"18","author":"Cai","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","first-page":"3504105","article-title":"An Automatic Target Detection Method Based on Multidirection Dictionary Learning for HFSWR","volume":"19","author":"Wang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"80716","DOI":"10.1109\/ACCESS.2020.2988796","article-title":"Unsupervised K-Means Clustering Algorithm","volume":"8","author":"Sinaga","year":"2020","journal-title":"IEEE Access."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shang, X., Li, X., Morales-Esteban, A., Asencio-Cort\u00e9s, G., and Wang, Z. (2018). Data Field-Based K-Means Clustering for Spatio-Temporal Seismicity Analysis and Hazard Assessment. Remote Sens., 10.","DOI":"10.3390\/rs10030461"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1109\/TPS.2015.2404439","article-title":"Analysis of Parameter Sensitivity of Induction Coil Launcher Based on Orthogonal Experimental Method","volume":"43","author":"Xiang","year":"2015","journal-title":"IEEE Trans. Plasma Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Nikolic, D., Stojkovic, N., and Lekic, N. (2018). Maritime over the Horizon Sensor Integration: High Frequency Surface-wave-radar and Automatic Identification System Data Integration Algorithm. Sensors, 18.","DOI":"10.3390\/s18041147"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/138\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:52:05Z","timestamp":1760147525000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,26]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010138"],"URL":"https:\/\/doi.org\/10.3390\/rs15010138","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,26]]}}}