A sea clutter detection method based on LSTM error frequency domain conversion
Yan Yan, Hongyan Xing
Abstract
Weak signal detection has always been a hot spot in the signal processing field. In this paper, the chaotic and large data size characteristics of sea clutter are analyzed, the advantage of the Long and Short Term Memory network (LSTM) is taken to design a weak signal detection method based on deep learning. The reconstructed phase space signal is used as the input of LSTM network, the length of training data is determined by embedding dimension and delay time, and a chaotic prediction model is established to detect weak signals from the prediction error. In order to improve the detection performance, reduce the missing rate of deep learning method for small feature signal, frequency domain conversion of the prediction error is conducted, the spectrum of the prediction error of different distance gates is compared to locate the coordinates of the weak signal. The experimental results show that the sea clutter detection method based on LSTM prediction error frequency domain conversion has strong applicability and higher accuracy, and the detection performance is improved by 30%.