RX Anomaly Detector With Rectified Background

This is the authors' abstract. We don't add key points for this paper.

An improved version of Reed-Xiaoli (RX) detector is proposed in this letter, which uses the benefits of median-mean line (MML) metric. The background data may be contaminated by anomalies. The anomalous outliers contributed in the estimate of background statistics decrease the differences between anomalous targets and background clutter. So, the performance of an RX detector is degraded. To deal with the negative effects of anomalous outliers, and to rectify the position of background data, the MML metric is used for providing more reliable background samples. Therefore, more stable background statistics (mean and covariance matrix) are estimated. The experimental results show the better performance of the proposed MML-RX method compared with some state-of-the-art anomaly detection methods with reasonable computation time.