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 (JR) 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 article proposes a multilabel learning-based lightweight compound JR algorithm to solve these problems, including three key steps. First, the proposed method performs de-chirp, time-frequency (TF) 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 multilabel discriminant thresholds based on dual evaluation metrics and a genetic algorithm to improve the recognition effect. The experiment shows that the floating-point operations (FLOPs) of the proposed method are only 0.11%-57.19% of the existing JR 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.