Modified Matched Filter for Cloud Clutter Suppression

Authors:
W.A.C. Schmidt
Affiliations:
-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1990

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Abstract

The least-mean-square (LMS) filter has been developed as an alternative to the classical matched filter (MF) to address the clutter-spectrum issue. However, the output of the MF and the LMS processes is dependent on the scene energy and marginally dependent on the filter signal shape. An approach referred to as the modified matched filter (MMF) is presented. The MMF is a product of the LMS filter and a nonlinear operator known as the inverse Euclidean distance. The nonlinear operator modifies the LMS filter to improve its sensitivity to signal shape. A comparison indicates the relative merit of including shape detection in the LMS clutter-suppression process. Infrared cloud scenes from the background measurements and analysis program (BMAP) were used to demonstrate the relative clutter-suppression performance for both the LMS and the MMF processes. A performance metric is developed to measure cloud clutter suppression quantitatively.