Shape Parameter Estimation of K-Distributed Sea Clutter Using Neural Network and Multisample Percentile in Radar Industry

In this paper, we consider the problem of robustly and accurately estimating the shape parameters ofK-distributed sea clutter in the maritime radar industry. Outliers formed by non-sea-surface echoes have a significant negative impact on the estimation accuracy. To improve the estimation performance, we first propose a bipercentiles feedforward neural network for the shape parameterη(BP-FFNN-η), which utilizes a ratio of two percentiles and a two-hidden-layer feedforward neural network. The BP-FFNN-ηcan learn the mathematical relationship between the shape parameter and the ratio of two percentiles, and can work in environments where the number of outliers is approximately known. Moreover, to solve the case where the number of outliers is not known due to dynamic changes in the environment, we also design another neural network (referred to as MBP-FFNN-η), which consists of multiple BP-FFNNs-ηand a multi-class classification network. The MBP-FFNN-ηcan perceive the change in the proportion of outliers, so an accurate estimate can be obtained from an unaffected BP-FFNN-η. Finally, training and test data are constructed to train and evaluate the proposed methods, respectively. Experimental results demonstrate that the BP-FFNN-ηperforms better than traditional moments-based estimators, and has almost the same performance as the tri-percentile estimator. Compared with the tri-percentile estimator, the BP-FFNN-ηavoids table lookups, and produces a continuous estimate. The MBP-FFNN-ηcan achieve more than 97% overall classification accuracy on simulated and measured data, and thus an accurate estimate of the shape parameter can be obtained when the number of outliers varies.