Attitude anomaly detection method for spatial target RCS sequence

Accurate and fast judgment of the abnormality of the space target attitude motion mode is of great significance for the monitoring of space targets. Aiming at the spatial target radar cross section (RCS) sequence, an unsupervised machine learning anomaly detection method based on wavelet packet decomposition (WPD) energy spectrum characteristics was proposed, and the one-class support vector machine (OCSVM) was adopted to verify the anomaly detection effect. Several typical anomaly scenes were set up for simulation analysis. The experimental results show that the method can effectively detect the abnormal attitude of the three-axis stable space object with unstable rotation. Compared with traditional statistical parameter features, wavelet transform statistical parameter features and energy feature of attitude discrimination method, it has the characteristics of high detection probability and good robustness.