Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Independent component analysis: algorithms and applications
Neural Networks
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Detection of weak stochastic signals in non-Gaussian noise: a general result
ICASSP '94 Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04
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Energy detectors are optimum to detect uncorrelated Gaussian signals or generalized likelihood ratio tests to detect completely unknown signals; in both cases, background noise must be uncorrelated Gaussian. However, energy detectors degrade when background noise is non-independent and non-Gaussian. An extension is presented in this paper to deal with this situation. Independence is achieved by means of a matrix linear transformation derived from independent component analysis. Non-Gaussianity is avoided by applying a scalar non-linear function to every element of the linearly transformed observation vector. Practical procedures for estimating the linear and nonlinear transformations are given in the paper. A SNR enhancement factor has been defined for the weak signal case, which is indicative of the expected improvement of the proposed extension. Some simulations illustrate the achieved improvements.