Research on space object recognition algorithm based on radar RCS data
In recent years,deep learning has achieved breakthrough progress in radar target recognition. However,research on deep learning target recognition algorithms based on radar cross-section (RCS) data is relatively scarce. Additionally,space target radar signals are easily affected by noise,resulting in low target recognition accuracy. This paper proposes an end-to-end Time-Frequency Feature Fusion Neural Network (TFF-Net) for space target recognition based on RCS sequence data. First,time-frequency analysis methods are used to convert the RCS sequence data into two-dimensional time-frequency data to reduce noise interference. Then,TFF-Net is used to extract deep features from the time-frequency data. TFF-Net first uses a convolutional neural network to capture spatial features of the targets,then employs a bidirectional long short-term memory network to model temporal information,and finally applies a temporal attention network to adaptively focus on important sequences in the time-frequency data. Comparative experiments were conducted on a space target dataset. The results show that the proposed algorithm achieves a space target recognition accuracy of 95.8％,significantly higher than several current mainstream radar target recognition algorithms. Furthermore,the classification accuracy under low signal-to-noise ratio conditions is also superior to other algorithms,demonstrating better noise robustness.
radar target recognitionradar cross sectiontime frequency analysisneural networks
万方数据
维普
