A DNN-Based Method for Sea Clutter Doppler Parameters Prediction

With the dramatic development of information technology and rapid growth of computation performances, artificial intelligent techniques have been gradually applied in all aspects of industrial research, especially in radar signal processing. However, deep learning methods utilized in radar sea clutter are just beginning, and related researches on Doppler characteristics of sea clutter remain sparse. In this paper, artificial intelligent research on sea clutter Doppler parameters prediction is developed based on real data. Firstly, classical signal processing methods for sea clutter spectral parameters extraction are introduced. Secondly, a deep neural network model is built to predict sea clutter Doppler parameters. Finally, the raised DNN model is compared to three other classical machine learning models which are widely used in regression prediction. After comprehensive comparisons with other models in different metrics, it can be concluded that DNN model built in this paper achieves better prediction results.