Multilabel Deep Learning-Based Lightweight Radar Compound Jamming Recognition Method

With the rapid development of electronic countermeasure technology, many active jamming compound scenes pose severe challenges to traditional radar, synthetic aperture radar (SAR), and other detection technologies. The accurate monitoring and recognition of individual jamming types contained in the complex electromagnetic environment can provide valuable prior information for radar countermeasures. However, existing jamming recognition algorithms suffer from huge models, fewer recognizable jamming types, and weak robustness, which is difficult to apply effectively to the resource-constrained airborne pulse signal real-time analysis instruments. This paper proposes a multi-label learning-based lightweight compound jamming recognition algorithm to solve these problems, including three key steps. First, the proposed method performs de-chirp, time-frequency transformation, and grayscale compression preprocessing for radar echoes. Then, an efficient hybrid attention (EHA) mechanism is designed and combined with ShuffleNet v2 to construct a recognition model. Finally, we generate independent multi-label discriminant thresholds based on dual evaluation metrics and a genetic algorithm to improve the recognition effect. The experiment shows that the floating-point operation (FLOPs) of the proposed method is only 0.11% ~ 57.19% of the existing jamming recognition methods, the overall recognition accuracy of the measured jamming data is 92.25%, higher than the existing methods of 7.37% ~ 16.73%, and has strong robustness to the fluctuation of radar waveform parameters.