Decision theory approach to nonlinear joint-transform correlation

We propose a new decision theory approach adapted to practical target detection and location tasks in which the spectral density of Gaussian additive noise is unknown. We determine the maximum likelihood and the maximum a posteriori solutions for that problem. We demonstrate that the nonlinear joint-transform correlation, which is frequently used in optical correlators, can be considered an approximation of these optimal processors. This new result constitutes a theoretical support in the context of detection theory for the use of nonlinearities in optical correlators.