In this work we prove that if the texture of compound-Gaussian clutter is modeled by an Inverse-Gamma distribution, the optimum detector is the optimum Gaussian matched filter detector compared to a data-dependent threshold that varies linearly with a quadratic statistic of the data. The compound-Gaussian model presented here varies parametrically from the Gaussian clutter model to a clutter model whose tails are evidently heavier than any K-distribution model. Moreover, we also show that the GLRT, which is a popular suboptimum detector due to its CFAR property, is in fact an optimum detector for our clutter model in the limit as the tails get extremely heavy.
New Results on Coherent Radar Target Detection in Heavy-Tailed Compound-Gaussian Clutter
GINI, FULVIO;GRECO, MARIA
2010-01-01
Abstract
In this work we prove that if the texture of compound-Gaussian clutter is modeled by an Inverse-Gamma distribution, the optimum detector is the optimum Gaussian matched filter detector compared to a data-dependent threshold that varies linearly with a quadratic statistic of the data. The compound-Gaussian model presented here varies parametrically from the Gaussian clutter model to a clutter model whose tails are evidently heavier than any K-distribution model. Moreover, we also show that the GLRT, which is a popular suboptimum detector due to its CFAR property, is in fact an optimum detector for our clutter model in the limit as the tails get extremely heavy.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.