Estimation of Sea Clutter Distribution Parameters Using Deep Neural Network

摘要

As a specific application of analytical methods on marine radar big data, this paper introduces deep learning theory into the field of sea clutter parameters estimation. A reasonable deep neural network model is built to estimate the parameters of amplitude distribution models so as to overcome the drawback of traditional methods based on statistical theory. In the proposed method, histogram method is used to preprocess the data, then deep neural network is trained with constructed dataset, and finally, parameter estimation results are obtained using test dataset. Validation results with simulation data and X-band radar-measured sea clutter data show that, compared with traditional estimation method, the deep neural network-based estimation method can improve parameter estimation accuracy significantly.