Abstract
In this paper, we propose that the foliage clutter follows log-logistic model using maximum likelihood (ML) parameter estimation as well as the root mean square error (RMSE) on PDF curves between original clutter and statistical model data. The measured clutter data is provided by Air Force Office of Scientific Research (AFOSR). In addition to investigating the log-logistic model, we also compare it with other popular clutter models, namely log-normal, Weibull and Nakagami. We show that the log-logistic model not only achieves the smallest standard deviation (STD) error on estimated model parameters, but also has the best goodness-of-fit and smallest RMSE. Further, the performance of detection at presence of foliage clutter is theoretically analyzed.
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