MINIMUM MEAN-SQUARE-ERROR FILTER FOR PATTERN-RECOGNITION WITH SPATIALLY DISJOINT SIGNAL AND SCENE NOISE

A minimum mean-square-error filter for pattern-recognition problems with input scene noise that is spatially disjoint (or nonoverlapping) with the target is described. The filter is designed to locate the target by producing a delta function output at the target position. The filter minimizes the mean square of the difference between the desired output delta function and the filter output in response to a noisy input data. We show that the filter output has a well-defined peak and small sidelobes in the presence of spatially disjoint target and scene noise.