DocumentCode :
1360758
Title :
Adaptive Threshold Estimation via Extreme Value Theory
Author :
Broadwater, Joshua B. ; Chellappa, Rama
Author_Institution :
Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD, USA
Volume :
58
Issue :
2
fYear :
2010
Firstpage :
490
Lastpage :
500
Abstract :
Determining a detection threshold to automatically maintain a low false alarm rate is a challenging problem. In a number of different applications, the underlying parametric assumptions of most automatic target detection algorithms are invalid. Therefore, thresholds derived using these incorrect distribution assumptions do not produce desirable results when applied to real sensor data. Monte Carlo methods for threshold determination work well but tend to perform poorly when targets are present. In order to mitigate these effects, we propose an algorithm using extreme value theory through the use of the generalized Pareto distribution (GPD) and a Kolmogorov-Smirnov statistical test. Unlike previous work based on GPD estimates, this algorithm incorporates a way to adaptively maintain low false alarm rates in the presence of targets. Both synthetic and real-world detection results demonstrate the usefulness of this algorithm.
Keywords :
Pareto distribution; adaptive estimation; signal detection; Kolmogorov-Smirnov statistical test; adaptive threshold estimation; extreme value theory; generalized Pareto distribution; signal detection; Constant false alarm rate; extreme value distributions; signal detection; statistics;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2009.2031285
Filename :
5229148
Link To Document :
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