Outlier-Robust Tri-Percentile Parameter Estimation Of K-Distributions

K-distribution is one of the most popular sea clutter amplitude models where the scale and shape parameters reflect the power level and non-Gaussianity of the clutter. In real oceanic environments, the two spatial-temporally varying parameters are estimated from sea clutter data with outliers. Outliers are probably from sea-surface ships, reefs, or abnormal scattering phenomena. Existing moment-based, numerical maximum likelihood, and [zlog(z)]-based estimators are sensitive to outliers, which results in abrupt degradation of parameter estimation precision in real clutter environments. This paper proposes an outlier-robust tri-percentile parameter estimator of K-distributions. It is shown that the ratio of two percentiles is a monotonically decreasing function of the shape parameter and independent of the scale parameter. In this way, the shape parameter is estimated from the ratio of two sample percentiles by the look-up table method and interpolation method. Moreover, an empirical formula on the optimal setup of the two percentiles is given by numerical computation. Next, the scale parameter is estimated by the third sample percentile whose position depends on the estimated shape parameter. Finally, the tri-percentile estimator is evaluated by using simulated and measured data, showing that it is rather robust to outliers in simulated data and raw sea clutter data. (C) 2020 Elsevier B.V. All rights reserved.