Shape Parameter Estimation of Radar K-distributed Sea Clutter Based on Support Vector Regression and Percentiles
In order to solve the problem that the estimation accuracy of traditional methods for estimating the shape parameters of radar K-distributed sea clutter is seriously degraded when there are outliers, a method for estimating the shape parameters of radar K-distributed sea clutter based on Support Vector Regression (SVR) and sample percentile ratio is proposed in this paper. Firstly, the clutter parameters and the percentile ranks are given, the sample percentile ratio and its logarithm are calculated according to the cumulative distribution function of the K distribution, and then an SVR model with the logarithm of the sample quantile ratio as input and the shape parameters to be estimated as output is established. The hyperparameters of SVR model are determined by cross-validation, and finally the SVR model is trained to estimate the shape parameter of K-distributed sea clutter robustly and accurately. The simulated and measured radar data show that the estimation error of the proposed method is lower than that of the conventional moment-based methods, and its estimation performance is similar to that of the percentile-based methods. Moreover,compared with the existing percentile-based methods, the hyperparameters of the proposed method are easy to determine, and it does not depend on table lookup.
万方数据
维普
