Jahangir, Mohammed; (2000) Coherent radar clutter statistics. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Ground surveillance radars have been used for a number of years for target detection and terrain mapping. Neither of these tasks can be carried out without a proper understanding of the ground clutter statistics. Traditionally, radar signal processing has been optimised for Gaussian interference. However, ground clutter in high resolution systems is rarely Gaussian. Thus in this thesis the non-Gaussian statistics of a coherent radar sensor are investigated. The clutter statistics are a function of both the terrain features and the imaging process. One of the major influencing factors is the radar resolution cell size. Thus as a prerequisite to studying the clutter characteristics the theory concerning radar resolution is discussed. A review of the non-Guassian clutter models is carried out and the K- distribution is selected as a reasonable model for land clutter statistics based on empirical evidence. However, a major drawback with the K-distribution is a lack of efficient optimum estimators for its parameters. Neural nets are devised to give an optimum estimator for the order parameter of the K-distribution. The minimum error is obtained with a net trained on the mean normalised log intensity and the amplitude contrast. A new estimator is pro- posed based on the multiple moments model. It is shown that nearly optimum performance is achieved with the new estimator. The parameter estimators are also investigated for a more general form of the K-distribution where the data statistics are such that a simple K-distribution is no longer sufficient to describe the clutter statistics. Estimators are proposed for the parameter values of generalised-K and homodyned-K based on a large order parameter approximation. Error analysis is carried out and a comparison is made between theoretical and simulated results.

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