Detection of Unknown Radar Pulses Using Spiking Neural Networks

In the context of electronic warfare (EW), we focus on the detection of unknown radar pulses with a superheterodyne receiver. The main objective is to temporally locate the radar pulses by a detection step at each time sample. To optimise the thresholding step, we introduce and assess preprocessing methods of the received signals. We aim to demonstrate the pertinence of deep‐learning methods, such as spiking neural networks (SNNs) for this task, compared to recurrent neural networks (RNNs) and long‐short term memory (LSTM). To do so, we first perform a theoretical comparison between the SNN, RNN and LSTM, especially in terms of energy consumption, which is crucial for embedded systems. To apply the SNN, we propose appropriate pre and postprocessing steps. We also develop suitable performance metrics. Then, we compare the three methods on a simulated database of radar pulses. After simulating a real environment, we transform the database by including different clutters (rural and maritime). We compare the different networks in similar situations, considering the distribution and the level of clutter, or the characteristics of the pulses. Although RNN fails to learn the detection task, other deep learning approaches provide better results than the standard methods. LSTM offers slightly better performance than SNN. However, we also conclude that SNN has a lower power consumption than RNN and LSTM networks. Therefore, the size of the SNN architecture can be increased to improve the results.