Innovations Based Detection Algorithm for Correlated Non-Gaussian Processes Using Multichannel Data,
ROME LAB GRIFFISS AFB NY
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This report addresses the problem of multichannel signal detection in additive, correlated, non-Gaussian noise using the innovations approach. While this problem has been addressed extensively for the case of additive Gaussian noise, the corresponding problem for the non-Gaussian case has received limited attention. This is due to the fact that there is no unique specification for the joint probability density function PDF of N correlated non-Gaussian random variables. We overcome this problem by using the theory of spherically invariant random processes SIRP and derive the innovations based detectors. It is found that the optimal estimators for obtaining the innovations processes are linear and that the resulting detector is canonical for the class of PDFs arising from SIRPs. Detection algorithms, Multichannel data. Non-Gaussian clutter, Statistics.