LMS and matched digital filters for optical clutter suppression

Abstract

Methods of computing impulse-response weights of 1- and 2-D matched filters and LMS filters for suppressing clutter in an electrooptic sensor's output are developed and illustrated with examples. The methods are applicable to signals from scanning or staring sensors viewing point or extended sources against variable backgrounds, provided signal shape and orientation are known. The matched-filter design technique is based on isotropic power-spectral clutter models whose parameters also must be known. Images of sensor output are assumed to provide the requisite information about signals and backgrounds. The LMS design technique is based on deterministic polynomial clutter models. An LMS filter estimates signal amplitude explicitly and local clutter parameters implicitly by performing a least-squares fit of a signal-plus-clutter model to the sensor output at every point of the scene. Thus clutter parameters need not be known for LMS design, although qualitative knowledge of the background may facilitate choice of the clutter model.