Light Gradient Boosting Machine-Based Low–Slow–Small Target Detection Algorithm for Airborne Radar
For airborne radar, detecting a low–slow–small (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 par...
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| Vydáno v: | Remote sensing (Basel, Switzerland) Ročník 16; číslo 10; s. 1737 |
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| Abstract | For airborne radar, detecting a low–slow–small (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. |
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| AbstractList | For airborne radar, detecting a low–slow–small (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. |
| Audience | Academic |
| Author | Huang, Pengcheng Liu, Jing Liao, Guisheng Juwono, Filbert H. Zeng, Cao Tao, Haihong Xu, Jingwei |
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| Cites_doi | 10.1109/TAES.1983.309350 10.1109/TGRS.2004.842481 10.1109/TGRS.2019.2923790 10.1109/TSP.2005.849172 10.1049/el:19960130 10.1109/RADAR.2013.6586083 10.1109/CISP-BMEI.2016.7852861 10.1109/TNN.2006.875985 10.1109/7.845254 10.1109/BIGSARDATA53212.2021.9574162 10.1109/ICIBA56860.2023.10165343 10.1016/j.atmosres.2012.02.007 10.1109/ICSP58490.2023.10248896 10.3390/rs14236021 10.1109/TAES.2004.1337463 10.23919/CISS51089.2021.9652263 10.1109/TSP.2011.2172435 10.1109/TAES.1986.310745 10.1109/RADAR.2014.6875798 10.3390/rs15133371 10.3390/rs15030864 10.1109/TAES.2003.1188894 10.1109/8.910535 10.1109/LGRS.2016.2635104 10.1109/TSP.2007.894238 10.1007/BF00994018 10.3390/s19153332 10.1038/323533a0 10.1109/TSP.2007.914345 10.1016/j.sigpro.2018.02.008 10.1109/LGRS.2023.3329687 10.1016/j.procs.2020.06.133 |
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| SubjectTerms | Airborne radar airborne radar target detection Airplanes Algorithms Altitude Artificial intelligence Classifiers Clutter clutter suppression Comparative analysis Computing time data collection Datasets Decision trees Design Feature extraction light gradient boosting machine low–slow–small target Machine learning Methods probability Radar Radar detection Radar equipment Real time space and time Support vector machines Target acquisition Target detection Uniqueness Unmanned aerial vehicles |
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