Modelling extremal events: for insurance and finance
Modelling extremal events: for insurance and finance
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Hybrid Detectors for Subpixel Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
The adaptive coherence estimator: a uniformly most-powerful-invariant adaptive detection statistic
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
On determining the radar threshold for non-Gaussian processes from experimental data
IEEE Transactions on Information Theory
The blind simulation problem and regenerative processes
IEEE Transactions on Information Theory
Rejection threshold estimation for an unknown language model in an OCR task
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Robust fusion: extreme value theory for recognition score normalization
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Improved switching CFAR detector for non-homogeneous environments
Signal Processing
Hi-index | 35.68 |
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.