Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network

Abstract

In the marine environment, shore-based radars play an important role in military surveillance and sensing. Sea clutter is one of the main factors affecting the performance of shore-based radar. Affected by marine environmental factors and radar parameters, the fluctuation law of sea clutter amplitude is very complicated. In the process of training a sea clutter amplitude prediction model, the traditional method updates the model parameters according to the current input data and the parameters in the current model, and cannot utilize the historical information of sea clutter amplitude. It is only possible to learn the short-term variation characteristics of the sea clutter. In order to learn the long-term variation law of sea clutter, a sea clutter prediction system based on the long short-term memory neural network is proposed. Based on sea clutter data collected by IPIX radar, UHF-band radar and S-band radar, the experimental results show that the mean square error of this prediction system is smaller than the traditional prediction methods. The sea clutter suppression signal is extracted by comparing the predicted sea clutter data with the original sea clutter data. The results show that the proposed sea clutter prediction system has a good effect on sea clutter suppression.

Evaporation Duct Height Nowcasting in China’s Yellow Sea Based on Deep Learning

“…The nowcasting of the EDH is based on the data of the previous period and can be expressed by the following equation [19]:…”

Multiscale Decomposition Prediction of Propagation Loss in Oceanic Tropospheric Ducts

“…In 2019, Zhao et al predicted sea-clutter power based on LSTM and achieved a lower prediction error than for a backpropagation (BP) NN [36]. Liwen et al predicted sea-clutter amplitude based on LSTM and achieved a smaller mean square error than the traditional prediction methods [37]. There are qualitative similarities between sea clutter and propagation loss.…”

Adaptive Clustering‐Based Marine Radar Sea Clutter Normalization

“…In recent years, researches about sea clutter suppression have been shown to achieve improving performance. An exhaustive review of sea clutter progress is outside the scope of our research, but we refer the interested readers to three comprehensive papers [1,5] and [6]. Related to our work, a brief review of sea clutter modeling, filtering, and prediction approaches is presented in the latter paragraphs.…”

“…Moreover, deep neural network (DNN) has been widely used in the radar signal processing field. To predict the amplitudes of sea clutter, Ma et al [6] taken the advantage of a long short-term memory (LSTM) neural network to found a sea clutter prediction system, which was composed of a preprocessing module followed by a prediction module and could learn the long-term variation characteristics of sea clutter. These approaches eliminated the effects of sea clutter to some extent and achieved improving performance.…”