ETED-Net: An End-to-End LPI Signal Detection Network With Neyman–Pearson Criterion Framework in Hybrid AWGN and Impulsive Noise Environment

Abstract

Low probability of intercept (LPI) signal detection is essential to cognitive electronic warfare (CEW) systems, but it is challenged by the coexistence of impulsive noise and the low signal-to-noise ratios (SNRs) stemming from LPI signals’ high time-bandwidth product and low peak power, thereby degrading the performance of traditional detection. Although recent deep neural network (DNN)-based methods show improvements, they lack principled false alarm control under Neyman-Pearson criterion (NPC) and overlook the impulsive noise. Besides, they fail to incorporate characteristics of LPI signals, and provide limited interpretability. To fill these gaps, this paper proposes an NPC-guided DNN detection framework. The key idea is to utilize DNN-based posterior probabilities as test statistic, achieving principled false-alarm control under NPC. The network is trained to extract discriminative LPI signal features, and detection threshold is computed using noise-only data. Building on this framework, we develop an end-to-end LPI signal detection network (ETED-Net), which integrates multi-scale feature extraction with channel attention to capture weak LPI signal features under hybrid AWGN and impulsive noise. Extensive experiments demonstrate that, within proposed framework, ETED-Net achieves superior detection performance and generalization over traditional and DNN-based methods. Furthermore, we demonstrate ETED-Net’s interpretability by tracing decisions to underlying signal structure.