Space Target Anomaly Detection based on Gaussian Mixture Model and Micro-Doppler Features

With the dramatic increase in human space activities, anomaly detection becomes an important issue in passive space target surveillance. In this article, an anomaly detection algorithm based on Gaussian mixture model and radar micro-Doppler features is proposed to detect the abnormal motion status of the space target. By coherent sampling and time-frequency analysis on the radar echo with additive white Gaussian noise corresponding to the normal motion statuses of the target, four micro-Doppler features are extracted and tested for normal distribution. Furthermore, the distribution of the multi-dimensional features and the corresponding parameters are fitted and estimated by Gaussian mixture model and expectation-maximization algorithm. Then, an anomaly detector is derived by solving for decision region using the fitted probability density function and a preset confidence level. Experimental results show that the average anomaly detection rate of the proposed method is 16.7%, 19.1%, and 34.0% higher than the one-class support vector machine, the convex hull, and the convolutional autoencoder-based methods, respectively.