Parameter Estimation Processor for K-distribution Clutter Based on Deep Learning

Abstract

This paper concerns the problem of parameter estimating of K-distribution. In previous work only the shape parameter of K-distribution is estimated from which the scale parameter is calculated. Therefore, the accuracy of the estimated scale parameter is largely determined by the accuracy of shape parameter estimation. In order to decouple the estimation of scale and shape parameters, in this work, deep learning is considered as the main tool to achieve K-distribution parameters estimation as a regression task. Specifically, a parameter estimation processor combining CNN with LSTM is constructed. The ground truth of the two parameters are taken as labels, and the weighted losses of the two parameters construct the total loss function of the network training. The effectiveness and superiority of the proposed estimation processor are verified on the simulated data and the real sea clutter data.