Sea Clutter Feature Prediction and Parameters Inversion Using Deep Learning Model

Abstract

The characteristics of sea clutter in real marine environments in different sea areas play a vital role in military industry such as radar detection, remote sensing, SAR imaging, and situational awareness. In this paper, a deep neural network sea clutter model is proposed based on the sea clutter big data under the real marine environment to study the characteristics of sea clutter and parameter inversion. Based on the ERA-Interim reanalysis (2015-2017), a database of marine environmental elements in China's offshore waters was established, and a spatiotemporal prediction model for marine environmental elements was proposed to improve missing values. Considering the scattering mechanism of sea surface at different scales comprehensively, a large database of sea clutter time series of multi-scale real surface is established and comparison with the experimental data. Aiming at the coastal waters of China, the LSTM model and deep neural network (DNN) are used to establish the correlation model between marine environmental elements and sea clutter characteristics, and the prediction and parameter inversion of sea clutter characteristics based on sea clutter big data are studied. The results shown that the coefficients of determination of the predicted fitted curves for the HH and VV polarization amplitudes reach 0.9249 and 0.8872, respectively. The inversion of wave heights in different sea areas is the lowest in the South China Sea (accuracy rate of 78%) and the highest in the East China Sea (accuracy rate is 90%). The results of this paper can help improve the ability of sea surface remote sensing and sea clutter suppression.