ACTA AERONAUTICAET ASTRONAUTICA SINICA >
A CFAR outlier detection method under varying SNR conditions
Received date: 2015-07-20
Revised date: 2015-12-19
Online published: 2015-12-28
Supported by
National Natural Science Foundation of China (61002026)
Outlier detection, also called anomaly detection, is a commonly encountered problem in areas such as pattern recognition, machine intelligence and knowledge discovery. When environmental mismatch occurs and the signal-to-noise ratio (SNR) of data changes, the noise variances in testing and training instances will be different, resulting in the ineffectiveness of previous outlier detection methods which aim to control the false-alarm probability. To solve the problem, an outlier detection method based on normalized residual (NR) is proposed in this paper. This method first calculates the outlier detection threshold according to the desired false-alarm probability and the change of noise variance; second measures the NR value of the query pattern using training instances; and then compares this NR value with the predefined detection threshold to determine whether the query pattern is an outlier or not. The detection threshold defined in this paper is adaptive to the desired false-alarm probability and the varying noise variance, thus can realize constant false-alarm rate (CFAR) outlier detection under varying SNR conditions. Simulation experiments validate the superior performance of the proposed method on false-alarm probability controlling and outlier detection.
RU Xiaohu , LIU Zheng , JIANG Wenli , HUANG Zhitao . A CFAR outlier detection method under varying SNR conditions[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016 , 37(7) : 2259 -2268 . DOI: 10.7527/S1000-6893.2015.0348
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