Radar Intra-Pulse Modulation Fast Perception via Adaptive Random Magnitude Channel Pruning and Knowledge Distillation

Towards the intelligent perception of radar intra-pulse modulation signals in complex environments, this paper proposes a method for fast recognition and SINR estimation of radar intra-pulse modulation signals. First, the adaptive random magnitude channel pruning algorithm (ARMCP) is designed to compress the YOLOv8n baseline model, coupled with channel-wise knowledge distillation (CWD) using the YOLOv8x large model as a teacher to restore the pruned YOLOv8n’s accuracy. Subsequently, a hybrid approach integrating hyper color segmenter and MobileNetv2 is employed for feature extraction within identified regions of interest (ROIs), facilitating the training of an XGBoost estimator for precise SINR prediction. The experimental results show that even at the pruning rate of 80%, the intra-pulse modulation recognition accuracy can still reach 99.07%, with a recognition speed of 3.2 ms, surpassing existing mainstream models such as YOLOv10 and YOLOv11. Additionally, the SINR estimation error is minimized to 0.958%, offering a novel paradigm and technical reference for advanced radar signal processing.