Computation of LMS (Least-Mean-Square) and Matched Digital Filters for Optical Clutter Suppression

摘要

Methods of computing impulse-response weights of one- and two-dimensional matched filters and least-mean-square (LMS) filters for suppressing clutter in an electro-optic sensor's output are developed and illustrated with examples. The methods are applicable to signals from scanning or staring sensors viewing finite or point 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 and, implicitly, local clutter parameters 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, though qualitative knowledge of the background may facilitate choice of the clutter model.