YOLO-CJ: A Lightweight Network for Compound Jamming Signal Detection

To improve the jamming cognitive level of radar in the complex and changeable electromagnetic environment, this paper proposes a YOLO-CJ (YOLO network for compound jamming detection) lightweight network for compound jamming signal detection. Firstly, the compound jamming image dataset is constructed by signal 0000-0000 © 2022 IEEE model and short-time Fourier transform (STFT). Secondly, based on the YOLOv7-tiny lightweight baseline model, the designed SimSPPFCSPC module, Triplet attention mechanism, and the C2f module are illustrated to improve the YOLOv7-tiny and establish the YOLO-CJ lightweight network, which aims to enhance the saliency and difference of the extracted features in the case of low SNR/JNR while effectively control the quantity of computation and parameters. Subsequently, combined with the compound jamming image dataset, the ablation experiment, algorithm comparison experiment, and robustness verification are carried out. The results demonstrate that the detection precision (mAP) and the inference time of a single image of the YOLO-CJ model can reach 98.69% and 12.73 ms, and the trade-off value of detection precision and speed can reach the highest 79.93%. Moreover, the mAP of the YOLO-CJ for compound jamming detection under SNR / JNR ≥ 0dB can reach more than 96%, which has good robustness and generalization ability under low SNR and JNR. Source code is released at https://github.com/zhuxuan-96/YOLO-CJ.