Issue No. 06 - June (1990 vol. 12)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.56196
<p>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.</p>
least mean square filter; matched filter; cloud clutter suppression; signal shape; inverse Euclidean distance; background measurements and analysis program; performance metric; filtering and prediction theory; pattern recognition; picture processing; spectral analysis
W. Schmidt, "Modified Matched Filter for Cloud Clutter Suppression," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 12, no. , pp. 594-600, 1990.