Shape Parameter Estimation of Radar K-distributed Sea Clutter Based on Support Vector Regression and Percentiles
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摘要: 针对传统的雷达K分布海杂波形状参数估计方法在异常样本存在情况下估计精度严重下降的问题，该文提出一种基于支持向量回归(SVR)和样本分位数比值的K分布海杂波形状参数估计方法。首先给定K分布杂波参数和分位数位置的值，根据K分布的累积分布函数计算样本分位数比值及其对数，然后建立以样本分位数比值对数为输入、待估计形状参数为输出的SVR模型，通过交叉验证确定SVR模型的超参数，最后训练SVR模型实现对K分布海杂波形状参数的稳健精确估计。仿真和实测雷达数据表明，所提方法的估计误差低于基于矩的估计方法的估计误差，并且与基于分位数的估计方法具有相近估计性能。此外，相比已有基于分位数的方法，所提方法的超参数容易确定，并且不依赖于查表。Abstract: 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.
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