The Generation of Correlated Weibull Clutter for Signal Detection Problems

A method is discussed for generating Weibull vectors with a desired correlation matrix and specified parameters. Such vectors may represent samples of a correlated clutter signal. The presented method makes use of a suitable nonlinear transformation of random Gaussian vectors with correlated components. Computational aspects of the method are also discussed.     

Testing the Independence of Nonhomogeneous Rayleigh Clutter

Two attractive real-time implementation tests are presented that discriminate between correlated and uncorrelated clutter. A clutter model is assumed in which the envelope distribution within a cell is Rayleigh, but the mean clutter level fluctuates from cell to cell. Both the tests utilize observations made in pairs on two clutter envelopes corresponding to two consecutive azimuth sweeps. The results are applicable to real-time testing of the effectiveness of various decorrelation techniques employed by radar systems.     

Estimators of Log-Normal Distribution Parameters

The maximum likelihood estimates of the median m and mean-to-median ratio ? for a log-normally distributed variate are derived, together with the asymptotic variances of these estimates. Using a Monte Carlo simulation, it is shown that these asymptotic variances of the estimates are reasonable when the number of sample points is N ? 50forp ? 8.     

Discrimination Between Log-Normal and Weibull Clutter

Two simple tests are presented for classifying a set of clutter samples into either the log-normal or Weibull distribution. The results obtained by Monte Carlo simulation have shown that both of these tests are only slightly inferior to the test based on the ratio of maximized likelihoods. An application to constant false-alarm rate (CFAR) processing is also discussed.