Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 60, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2022.3213277
Keywords
Feature extraction; Spaceborne radar; Radar; Satellites; Anomaly detection; Space vehicles; Radar cross-sections; Anomaly detection; Gaussian mixture model (GMM); micro-Doppler; space target
Categories
Funding
- National Natural Science Foundation of China [61925106, 62101304]
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An anomaly detection algorithm based on GMM and radar micro-Doppler features is proposed in this article, achieving a higher detection rate for abnormal motion status of space targets by utilizing normal distributed micro-Doppler features and fitting multidimensional feature distribution using the EM algorithm.
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 the Gaussian mixture model (GMM) and radar micro-Doppler features is proposed to detect the abnormal motion status of the space target. By coherent sampling and time-frequency (TF) analysis on the radar echo with additive white Gaussian noise (AWGN) 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 multidimensional features and the corresponding parameters are fit and estimated by the GMM and expectation-maximization (EM) algorithm. Then, an anomaly detector is derived by solving for the decision region using the fit probability density function (pdf) 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 (OCSVM), the convex hull, and the convolutional autoencoder (CAE)-based methods, respectively.
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