Radar Signal Recognition Based on CSRDNN Network

Abstract

It is essential to achieve the high-accuracy recognition of low probability of intercept (LPI) radar signals in modern electronic warfare. However, under low signal-to-noise ratio (SNR), the recognition accuracy of the LPI radar signals is relatively low. In this paper, a novel radar signal recognition method based on Convolutional Stacked Recurrent Deep Neural Network (CSRDNN) is proposed. Firstly, we design a Convolutional Neural Network (CNN) to expand the feature space of input time domain signals, the features extracted by CNN were then used as inputs of the Stacked Recurrent Neural Networks (SRNN) module. In the SRNN module, we sequentially stack GRU, LSTM, and BGRU, enabling the model to better handle the short-term and long-term dependence of signal features and effectively solve asynchronous problems in unidirectional RNN networks. Subsequently, a Fully Connected Deep Neural Network (FCDNN) was employed to accomplish the recognition task. In addition, we design a training algorithm composed of the Nesterov-Adaptive Moment Estimation (Nadam) algorithm and the CosineAnnealing Learning Rate (LR) adjustment strategy to improve the training efficiency of the model. The experimental results demonstrate that the proposed model has higher recognition accuracy at low SNR compared to other models, with an overall recognition accuracy of 92.96% at −4 dB.

Keywords

• Low probability of intercept
• radar signal recognition
• stacked recurrent neural networks
• activation function
• training algorithm