Radar pre-sorting algorithm based on autoencoder and LSTM

Abstract

As the electromagnetic environment becomes increasingly complex, most current radar signal sorting methods are unsustainable. They often perform poorly when dealing with unknown radar types and low-frequency radar pulse data. This paper introduces a radar pre-sorting algorithm based on autoencoder and LSTM. The algorithm utilizes multi-dimensional information such as pulse width, carrier frequency, and time of arrival. The autoencoder network is employed to achieve automatic feature extraction and clustering, enhancing the extraction of latent features in the data. The proposed network model mainly consists of three parts: an encoding module composed of a convolutional neural network (CNN), a feature aggregation module composed of long short-term memory (LSTM), and a decoding module obtained through a convolutional autoencoder, referred to as CLDE (CNN-LSTM-Decode). The encoding module extracts features from multi-dimensional data to obtain compressed features, the feature accumulation module processes the compressed features, further extracting hidden features between pulses. Subsequently, the decoding module determines the pulse modulation type of each pulse, achieving the purpose of radar pulse signal pre-sorting. Simulation results show that this network structure effectively pre-classifies unknown radar signals and has a high recognition rate for low-frequency pulses. Additionally, CLDE exhibits high reliability and stability in environments with pulse loss.