Intelligent Recognition and Parameter Estimation of Radar Active Jamming Based on Oriented Object Detection - Jiawei Lu; Yiduo Guo; Weike Feng; Xiaowei Hu; Jian Gong; Yu Zhang (Remote Sensing)
To enhance the perception capability of radar in complex electromagnetic environments, this paper proposes an intelligent jamming recognition and parameter estimation method based on deep learning. The core idea of the method is to reformulate the jamming perception problem as an object detection task in computer vision, and we pioneer the application of oriented object detection to this problem, enabling simultaneous jamming classification and key parameter estimation. This method takes the time–frequency spectrogram of jamming signals as input. First, it employs the oriented object detection network YOLOv8-OBB (You Only Look Once Version 8–oriented bounding box) to identify three types of classic suppression jamming and five types of Interrupted Sampling Repeater Jamming (ISRJ) and outputs the positional information of the jamming in the time–frequency spectrogram. Second, for the five ISRJ types, a post-processing algorithm based on boxes fusion is designed to further extract features for secondary recognition. Finally, by integrating the detection box information and secondary recognition results, parameters of different ISRJ are estimated. In this paper, ablation experiments from the perspective of Non-Maximum Suppression (NMS) are conducted to simulate and compare the OBB method with the traditional horizontal bounding box-based detection approaches, highlighting OBB’s detection superiority in dense jamming scenarios. Experimental results show that, compared with existing jamming detection methods, the proposed method achieves higher detection probabilities under the jamming-to-noise ratio (JNR) ranging from 0 to 20 dB, with correct identification rates exceeding 98.5% for both primary and secondary recognition stages. Moreover, benefiting from the advanced YOLOv8 network, the method exhibits an absolute error of less than 1.85% in estimating six types of jamming parameters, outperforming existing methods in estimation accuracy across different JNR conditions.