A MFR Work Modes Recognition Method Based on Dual-Scale Feature Extraction

Multi-function radar (MFR) work modes recognition is an important research component of the electronic reconnaissance field. When facing MFR systems equipped with complex mode-waveform mapping relationships and flexible beam scanning techniques, the intercepted work mode pulse sequences have a wide temporal range of feature distributions and variable durations, which bring significant challenges for accurate recognition. To address this issue, this study constructs a novel hierarchical MFR signal model with waveform multiplexing and waveform scheduling laws with spatial beam arrangement and proposes a work mode recognition method based on dual-scale feature extraction. The recognition method first obtains the variable-length sequence processing capability through pulse sequence segmentation. Then, a structure composed of convolutional neural network (CNN) and long short-term memory (LSTM) is followed to extract the deep time-series features at the internal-segment scale of segments, and the features of each segment are concatenated in the time dimension. Subsequently, an LSTM-Attention network is used to extract the external-segment-scale features while adaptively assigning a higher weight to important waveform segments. Ultimately, the work mode recognition results are obtained. The experimental results show that the proposed method’s performance is advantageous in recognizing work modes under the comprehensive MFR signal model.