Fusion recognition of radar emitter signals based on multiple transform domain features

Abstract:

In response to the problems of low information utilization and poor anti-noise performance in existing recognition methods for radar emitter signals of complex systems, we proposed an ensemble deep neural network recognition method integrating multiple transform domain features of radar emitter signals. Firstly, based on the three transform domain methods of bispectrum estimation, ambiguity function (AF), and Hilbert-Huang transform (HHT), we processed the emitter signals, extracted, and transformed the signal’s rectangular integral bispectrum feature, AF orthogonal slice feature, and Hilbert marginal spectrum feature into two-dimensional feature images with stronger expressiveness and interpretability. Then, we constructed a fusion recognition model framework based on ResNet18 + multilayer perceptron (MLP), took multiple ResNet18 as base learners to perform primary recognition on the datasets of three transform domain features, and obtained feature vectors represented by probabilities. Finally, we conducted fusion learning on the feature vectors via the MLP and output the final signal category information. The experimental results show that the proposed method maintains an overall average recognition rate of above 99.23% for six classes of radar emitter signals at a signal-to-noise ratio (SNR) of 0 dB. Even in the low SNR environment of -4 dB, the recognition rate remains stable at above 96.54%. The results verify the effectiveness and better performance of the proposed method.