Prediction of Sea Clutter Based on Recurrent Neural Network

Abstract

As radar sea clutter has chaotic characteristics, a prediction model can be established to predict sea clutter according to short-term predictability of chaotic time series. Therefore, this paper proposes a sea clutter amplitude prediction model based on LSTM, Stacked LSTM and Nested LSTM. This paper first obtains the best state space for sea clutter prediction according to the phase space reconstruction theory and establishes the prediction equation, then uses three networks to predict the sea clutter amplitude, and finally compares the effects of the three neural networks and analyzes the factors that affect the prediction precision. The experimental results on the IPIX radar data and P-band radar data show that, the model used in this paper has a smaller prediction error than RBF, and the prediction performance is better. The model can achieve high-precision and high-efficiency prediction of sea clutter and has verified the effectiveness of the method.