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Fusion of Distributions for Radar Clutter Modeling

Abstract : To recognize an object in an image, an algorithm must identify not only the object pixels, but also non-object clutter pixels. Non-object pixels can be assessed with a priori clutter models that account for the varying terrain and cultural objects. Radar clutter models have been well developed; however, these models typically incorporate a single distribution to capture background effects. In this paper, we propose to use a fusion of distributions using an additive mixture model to characterize various background clutter information so as to more accurately develop a clutter model useful for object recognition. In a radar example, we show a fused distribution using a Rayleigh and Pareto model describing the average and heavy tail clutter characteristics.