Abstract:

Aiming at the problem that the traditional target image anomaly detection method based on radar cross-section extracts effective features with difficulty and the recognition effect is poor, an anomaly detection method gated recurrent unit deep neural network model is proposed. First, the method uses the sliding window method to divide the dynamic radar cross-sectional sequence. Then, to complete the adaptive feature learning of the input sequence, the gated recurrent unit deep neural network is used. Finally, the full connection layer is used to realize the satellite attitude anomaly detection. Simulation results show that the proposed method can achieve a high feature discrimination degree, that it can effectively detect the unstable rolling satellite compared with the traditional method, and that it has strong noise robustness.

Key words: gated recurrent unit, radar cross-sectional, satellite target, anomaly detection