CFAR Outlier Detection With Forward Methods

Separation or classification of signal-present samples from noise-only samples is studied. The false-alarm probability implies how many noise-only samples are wrongly classified as outliers, and typically it should be smaller than some upper limit. The noise distribution parameters are not known a p...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2007-09, Vol.55 (9), p.4702-4706
Main Authors: Lehtomaki, J.J., Vartiainen, J., Juntti, M., Saarnisaari, H.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Classification
Cognitive radio
constant false-alarm rate (CFAR)
Decision making
detection threshold
Detection, estimation, filtering, equalization, prediction
Detectors
Estimators
Exact sciences and technology
Information, signal and communications theory
Light rail systems
Noise
Noise robustness
Probability
Radar detection
Robust control
Separation
Signal and communications theory
signal detection
Signal processing
Signal processing algorithms
Signal, noise
Statistics
Telecommunications and information theory
Testing
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Summary:Separation or classification of signal-present samples from noise-only samples is studied. The false-alarm probability implies how many noise-only samples are wrongly classified as outliers, and typically it should be smaller than some upper limit. The noise distribution parameters are not known a priori and have to be estimated. Multiple outliers have a strong influence to that estimation and may lead to uncontrollable false-alarm probability. The false-alarm probability control can be improved by robust estimators and/or by forward-detection methods. In this article, the false-alarm probability of the forward methods is analyzed. The forward consecutive mean excision (FCME) algorithm is enhanced to allow better false-alarm control. It is proposed that the forward method using the cell-averaging (CA) constant false-alarm rate (CFAR) technique can be applied for locating the outliers. The results show that its false-alarm probability stays close to the required value even in the presence of multiple outliers.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2007.896239