Elements of information theory
Elements of information theory
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Elements of signal detection and estimation
Elements of signal detection and estimation
Signal processing with alpha-stable distributions and applications
Signal processing with alpha-stable distributions and applications
The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Kernel independent component analysis
The Journal of Machine Learning Research
A mutual information extension to the matched filter
Signal Processing - Special issue: Information theoretic signal processing
On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions
IEEE Transactions on Computers
Robust Multidimensional Matched Subspace Classifiers Based on Weighted Least-Squares
IEEE Transactions on Signal Processing
Generalized correlation function: definition, properties, and application to blind equalization
IEEE Transactions on Signal Processing - Part I
Efficient estimation of Class A noise parameters via the EM algorithm
IEEE Transactions on Information Theory
Two-step fuzzy logic-based method for impulse noise detection in colour images
Pattern Recognition Letters
A regularized correntropy framework for robust pattern recognition
Neural Computation
A survey on computing Lévy stable distributions and a new MATLAB toolbox
Signal Processing
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This paper demonstrates the effectiveness of a nonlinear extension to the matched filter for signal detection in certain kinds of non-Gaussian noise. The decision statistic is based on a new measure of similarity that can be considered as an extension of the correlation statistic used in the matched filter. The optimality of the matched filter is predicated on second order statistics and hence leaves room for improvement, especially when the assumption of Gaussianity is not applicable. The proposed method incorporates higher order moments in the decision statistic and shows an improvement in the receiver operating characteristics (ROC) for non-Gaussian noise, in particular, those that are impulsive distributed. The performance of the proposed method is demonstrated for detection in two types of widely used impulsive noise models, the alpha-stable model and the two-term Gaussian mixture model. Moreover, unlike other kernel based approaches, and those using the characteristic functions directly, this method is still computationally tractable and can easily be implemented in real-time.