Low Radar Cross Section Target Detection and Classification for Pulse Radar Systems using RPCA and Deep Learning

Abstract

In coastal environments, detecting and classifying low radar cross-section (RCS) moving targets such as dinghies and trawlers using pulse radar system is challenging due to strong sea and land clutter. The clutter reduces detection accuracy. Further, the detection and classification heavily depend on radar operator expertise. To overcome these limitations, we propose an operator-independent framework for detection and classification. This paper presents a hybrid approach that combines Robust Principal Component Analysis (RPCA) with deep learning-based object detection models to improve target detection using radar Plan Position Indicator (PPI) images. RPCA is first applied as a low rank nature clutter separation from sparse moving targets. Detection is then performed using You Only Look Once (YOLO) and Detection Transformer (DETR) models on these clutter-suppressed representations. Experimental results show that YOLO achieves a precision 0.66, recall of 0.65 with an mAP@0.5 of 0.71, while DETR provides improved performance, reaching a precision 0.75, recall of 0.73 with an mAP@0.5 of 0.77. These results indicate that transformer-based models perform better for detection and classification of moving targets in RPCA-processed radar PPI echo images, thereby enhancing detection capability for coastal surveillance applications.