{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:04:08Z","timestamp":1760144648843,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T00:00:00Z","timestamp":1715644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Radar Signal Processing Laboratory","award":["KGJ20230X","61931016","62071344"],"award-info":[{"award-number":["KGJ20230X","61931016","62071344"]}]},{"name":"NSFC","award":["KGJ20230X","61931016","62071344"],"award-info":[{"award-number":["KGJ20230X","61931016","62071344"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>For airborne radar, detecting a low\u2013slow\u2013small (LSS) target is a hot and challenging topic, which results from the rapidly increasing number of non-cooperative flying LSS targets becoming of widespread concern, and the low signal-to-clutter ratio (SCR) of LSS targets results in the targets being particularly easily overwhelmed by the clutter. In this paper, a novel light gradient boosting machine (LightGBM)-based LSS target detection algorithm for airborne radar is proposed. The proposed method, based on the current real-time clutter environment of the range cell to be detected, firstly designs a specific real-time space-time LSS target signal repository with special dimensions and structures. Then, the proposed method creatively designs a new fast-built real-time training feature dataset specifically for the LSS target and the current clutter, together with a series of unique data transformations, sample selection, data restructuring, feature extraction, and feature processing. Finally, the proposed method develops a unique machine learning-based LSS target detection classifier model for the designed training dataset, by fully excavating and utilizing the advantages of the ensemble decision trees-based LightGBM. Consequently, the pre-processed data in the range cell of interest are classified using the proposed algorithm, which achieves LSS target detection by evaluating the output results of the designed classifier. Compared with the traditional classical target detection methods, the proposed algorithm is capable of providing markedly superior performance for LSS target detection. With an appropriate computational time, the proposed algorithm attains the highest probability of detecting LSS targets under the low SCR. The simulation outcomes and detection results with the experimental data are employed to validate the effectiveness and merits of the proposed algorithm.<\/jats:p>","DOI":"10.3390\/rs16101737","type":"journal-article","created":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T08:59:37Z","timestamp":1715677177000},"page":"1737","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Light Gradient Boosting Machine-Based Low\u2013Slow\u2013Small Target Detection Algorithm for Airborne Radar"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2205-8759","authenticated-orcid":false,"given":"Jing","family":"Liu","sequence":"first","affiliation":[{"name":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Pengcheng","family":"Huang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5842-3629","authenticated-orcid":false,"given":"Cao","family":"Zeng","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Guisheng","family":"Liao","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1865-6214","authenticated-orcid":false,"given":"Jingwei","family":"Xu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Haihong","family":"Tao","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Filbert H.","family":"Juwono","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215123, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/TAES.2003.1188894","article-title":"Robust space-time adaptive processing for airborne radar in nonhomogeneous clutter environments","volume":"39","author":"Wang","year":"2003","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_2","unstructured":"Klemm, R. (2002). Principles of Space-Time Adaptive Processing, The Institution of Electrical Engineers."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1109\/7.845254","article-title":"STAP for clutter suppression with sum and difference beams","volume":"36","author":"Brown","year":"2000","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1049\/el:19960130","article-title":"Space-time joint processing method for simultaneous clutter and jamming rejection in airborne radar","volume":"32","author":"Wang","year":"1996","journal-title":"Electron. Lett."},{"key":"ref_5","unstructured":"Dipietro, R.C. (1992, January 26\u201328). Extended factored space-time processing for airborne radar systems. Proceedings of the 26th Asilomar Conference, Pacific Grove, CA, USA."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, J., Liao, G.S., Xu, J.W., Zhu, S.Q., Juwono, F.J., and Zeng, C. (2022). Autoencoder neural network-based STAP algorithm for airborne radar with inadequate training samples. Remote Sens., 14.","DOI":"10.3390\/rs14236021"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2477","DOI":"10.1109\/TSP.2005.849172","article-title":"Sparse solutions to linear inverseproblems with multiple measurement vectors","volume":"53","author":"Cotter","year":"2005","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1109\/LGRS.2016.2635104","article-title":"A novel STAP based on spectrum-aided reduced-dimension clutter sparse recovery","volume":"14","author":"Han","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2346","DOI":"10.1109\/TSP.2007.914345","article-title":"Bayesian compressive sensing","volume":"56","author":"Ji","year":"2008","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.sigpro.2018.02.008","article-title":"Airborne radar space time adaptive processing based on atomic norm minimization","volume":"148","author":"Feng","year":"2018","journal-title":"Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/TSP.2011.2172435","article-title":"L1-regularized STAP algorithms with a generalized sidelobe canceler architecture for airborne radar","volume":"60","author":"Yang","year":"2011","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_12","unstructured":"Maria, S., and Fuchs, J.J. (2006, January 4\u20138). Detection performance for the GMF applied to STAP data. Proceedings of the 2006 14th European Signal Processing Conference, Florence, Italy."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ma, Z., Liu, Y., Meng, H., and Wang, X. (May, January 29). Jointly sparse recovery of multiple snapshots in STAP. Proceedings of the 2013 IEEE Radar Conference (RadarCon13), Ottawa, ON, Canada.","DOI":"10.1109\/RADAR.2013.6586083"},{"key":"ref_14","first-page":"414","article-title":"Adaptive detection mode with threshold control as a function of spatially sampled clutter-level estimates","volume":"29","author":"Finn","year":"1968","journal-title":"RCA Rev."},{"key":"ref_15","first-page":"750","article-title":"Range resolution of targets using automatic detectors","volume":"14","author":"Trunk","year":"1978","journal-title":"IEEE Trans. AES"},{"key":"ref_16","unstructured":"Hansen, V.G. (1973, January 23\u201325). Constant false alarm rate processing in search radars. Proceedings of the IEEE International Radar Conference, IEEE Radar Present and Future, London, UK."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1109\/TAES.1983.309350","article-title":"Radar CFAR thresholding in clutter and multiple target situations","volume":"19","author":"Rohling","year":"1983","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1109\/TAES.1986.310745","article-title":"An adaptive detection algorithm","volume":"AES-22","author":"Kelly","year":"1986","journal-title":"IEEE Trans. Aerosp. Electron."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3577","DOI":"10.1109\/TSP.2007.894238","article-title":"Rao test for adaptive detection in Gaussian interference with unknown covariance matrix","volume":"55","author":"De","year":"2007","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1109\/TAES.2004.1337463","article-title":"Statistical analysis of real clutter at different range resolutions","volume":"40","author":"Conte","year":"2004","journal-title":"IEEE Trans. Aerosp. Electron."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ball, J.E. (2014, January 19\u201323). Low signal-to-noise ratio radar target detection using Linear Support Vector Machines (L-SVM). Proceedings of the 2014 IEEE Radar Conference, Cincinnati, OH, USA.","DOI":"10.1109\/RADAR.2014.6875798"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1109\/TNN.2006.875985","article-title":"Nonlinear Spatial-temporal Prediction Based on Optimal Fusion","volume":"17","author":"Xia","year":"2006","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.atmosres.2012.02.007","article-title":"Artificial Intelligence Techniques for Clutter Identification with Polarimetric Radar Signatures","volume":"109","author":"Islam","year":"2012","journal-title":"Atmos. Res."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chai, B., Chen, L., Shi, H., and He, C. (2021, January 22\u201324). Marine ship detection method for SAR image based on improved faster RCNN. Proceedings of the 2021 SAR in Big Data Era (BIGSARDATA), Nanjing, China.","DOI":"10.1109\/BIGSARDATA53212.2021.9574162"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2023.3329687","article-title":"A multichannel SAR ground moving target detection algorithm based on subdomain adaptive residual network","volume":"20","author":"Zhang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, S., Li, J., Wang, Y., and Li, Y. (2016, January 15\u201317). Radar HRRP target recognition based on Gradient Boosting Decision Tree. Proceedings of the 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Datong, China.","DOI":"10.1109\/CISP-BMEI.2016.7852861"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Cheng, D., Qi, W., Song, R., Yu, C., and Liu, S. (2023, January 26\u201328). Radar target recognition of individuals based on XGBoost. Proceedings of the 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China.","DOI":"10.1109\/ICIBA56860.2023.10165343"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Guan, S., Gao, X., Lang, P., and Dong, J. (2023, January 21\u201323). The corner reflector array recognition based on multi-domain features extraction and CatBoost. Proceedings of the 2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi\u2019an, China.","DOI":"10.1109\/ICSP58490.2023.10248896"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TGRS.2004.842481","article-title":"Investigation of the random forest framework for classification of hyperspectral data","volume":"43","author":"Ham","year":"2005","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, Q., Feng, Y., Guan, L., Wu, W., Wang, S., and Li, Q. (2023). X-Band radar attenuation correction method based on LightGBM algorithm. Remote Sens., 15.","DOI":"10.3390\/rs15030864"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cai, L., Qian, H., Xing, L., Zou, Y., Qiu, L., Liu, Z., Tian, S., and Li, H. (2023). A Software-Defined Radar for Low-Altitude Slow-Moving Small Targets Detection Using Transmit Beam Control. Remote Sens., 15.","DOI":"10.3390\/rs15133371"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/j.procs.2020.06.133","article-title":"A double-threshold target detection method in detecting low slow small target","volume":"174","author":"Yu","year":"2020","journal-title":"Proc. Comput. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"8937","DOI":"10.1109\/TGRS.2019.2923790","article-title":"Low-velocity small target detection with Doppler-guided retrospective filter in high-resolution radar at fast scan mode","volume":"57","author":"Shi","year":"2019","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Guo, K., Zheng, X., Shi, S., Qin, K., and Xie, T. (2021, January 3\u20135). A low-slow-small target detection method for offshore radar based on GPU. Proceedings of the 2021 2nd China International SAR Symposium (CISS), Shanghai, China.","DOI":"10.23919\/CISS51089.2021.9652263"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Musa, S.A., Abdullah, R.S., Sali, A., Ismail, A., and Abdul Rashid, N.E. (2019). Low-slow-small (LSS) target detection based on micro Doppler analysis in forward scattering radar geometry. Sensors, 19.","DOI":"10.3390\/s19153332"},{"key":"ref_36","first-page":"39","article-title":"Evaluation of classification models in machine learning","volume":"7","author":"Novakovic","year":"2017","journal-title":"Theory Appl. Math. Comput. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1109\/8.910535","article-title":"A deterministic least-squares approach to space-time adaptive processing","volume":"49","author":"Sarkar","year":"2001","journal-title":"IEEE Trans. Antennas Propag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1737\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:42:14Z","timestamp":1760107334000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1737"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,14]]},"references-count":39,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16101737"],"URL":"https:\/\/doi.org\/10.3390\/rs16101737","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,5,14]]}}}