Radar Signals Intrapulse Modulation Recognition Using Phase-Based STFT and BiLSTM

Abstract

The goal of Radar Emitter Recognition (RER) is to extract the features of a received emitter signal. It has become a critical issue as new radar types are emerging and the electromagnetic environment is becoming more dense and complex. Deep Neural Networks (DNNs) have recently proven effective for emitter identification, however, recognition of phase-coded waveforms at a low Signal to Noise Ratio (SNR) remains a challenge. In this paper, a novel phase-based RER approach using Short Time Fourier Transform (STFT) and Bidirectional Long Short Term Memory (BiLSTM) is proposed while enhancing ability of learning features from noisy signals. The phase spectrum of phase-coded signals is analyzed in contrast to the amplitude spectrum used in the state-of-the-art approaches in the literature. The derived phase-based features are directly provided as an input to the proposed BiLSTM architecture. A fully connected layer follows the BiLSTM layer. Finally, a softmax classifier is employed to accomplish the recognition task. Six distinct types of phase-coded waveforms degraded by Additive White Gaussian Noise (AWGN) with SNRs ranging from -8dB to 8dB are simulated. The suggested method in this research involves simple pre-processing and exhibits overall recognition accuracy of more than 90% at SNR of -2dB.