Outlier-robust tri-percentile parameter estimation of compound-Gaussian clutter with lognormal distributed texture - Feng, Tian; Shui, Peng-Lang (Digital signal processing, 2022)
Compound-Gaussian model with lognormal distributed texture (CG-LNT) is one of effective amplitude distribution models to characterize high-resolution sea clutter and therefore it is important for high-resolution maritime radars to robustly estimate the parameters of the CG-LNT model from raw radar data, a mixture of sea clutter data and a small number of outliers. Existing estimators, such as moment-based and [zlog(z)]-based ones, are sensitive to outliers and fail to use in practical radars. In this paper, an outlier-robust tri-percentile estimator is proposed to realize the robust parameter estimation of the CG-LNT model of sea clutter. It is shown that the ratio of two percentiles is a monotonic function of the shape parameter and independent of the scale parameter, though without analytical expressions. In this way, the shape parameter is estimated from the ratio of the two sample percentiles by the look-up table method. Later, the scale parameter is estimated by the third sample percentile that is determined by the estimated shape parameter. Moreover, it is found that the positions of the three percentiles affect the performance and thus two empirical formulae are given to improve the estimate precision by numerical computation. Finally, the experimental results using simulated and real sea clutter data show that the proposed tri-percentile estimator implements robust and fast estimation of the parameters of the CG-LNT distribution. •Tri-percentile estimators are proposed to estimate the parameters of CG-LNT model.•The empirical optimal parameter setup formulas are given to improve the precision.•Tri-percentile estimators attain fast and robust estimation than existing methods.