Novel multi-dimensional feature radar target detection method with deep forest

Kesai Ouyang; Yuhao Yang; Junpeng Yu

doi:10.1117/12.3029737

21 June 2024 Novel multi-dimensional feature radar target detection method with deep forest

Kesai Ouyang, Yuhao Yang, Junpeng Yu

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131673F (2024) https://doi.org/10.1117/12.3029737
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

Accurate target detection under heavy background noise and clutter remains the key challenge for radar applications. Traditional target detection methods based on statistical models have limited performance in complex environment. Although advanced deep learning methods such as Convolutional Neural Networks (CNN) have been introduced to suppress clutter and improve the target detection performance, the limited interpretability and flexibility hinders their further implication and extension. To address this challenge, A novel multi-dimensional feature target detection method based on deep forest (DF) is proposed in this paper. Firstly, a low threshold Constant False Alarm Rate (CFAR) detector is employed to preprocess the received radar signals. Afterwards, the multi-dimensional features of filtered signals are classified using the designed DF model, which could deeply express the difference between radar targets and clutter. Finally, comparison has been conducted against CFAR and CNN by using experimental data and the results indicate that the proposed method significantly improves radar target detection capability under complex environment.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kesai Ouyang, Yuhao Yang, and Junpeng Yu "Novel multi-dimensional feature radar target detection method with deep forest", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131673F (21 June 2024); https://doi.org/10.1117/12.3029737

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