A variation of the matched spatial filter (MSF) has been developed and tested. It is derived by a power-series expansion of the ideal MSF that, in the absence of background noise, produces a Dirac delta function peak at the detection location on the correlation plane. The motivation for the approximation is to design an MSF that produces a narrow correlation peak with reduced susceptibility to noise amplification in the filtering process. This new filter, which we will call the complement MSF, includes the intrinsic phase information of the reference signal and a magnitude term that can be truncated at any order. Experimental results show that the complement MSF produces correlation peaks that can be controlled by varying the order of approximation, and that these peaks may improve discrimination over classical matched filtering methods such as the familiar cross correlation. In addition, we have exploited the familiar Wiener-Helstrom method for inverse filtering to blend both classical MSFs and complement MSFs of different order for imaging scenarios where the noise power spectrum is known or can be estimated. Preliminary outputs using this technique have shown sharper correlation peaks and better noise floor suppression than yielded by implementing the blended components individually.
['Derek J. Walvoord', 'Roger L. Easton']
Variations of matched filtering for reduced noise amplification