Low probability of intercept (LPI) radar signals pose significant challenges for electronic support (ES) in Internet of Military Things (IoMT) due to low signal-to-noise ratios (SNRs) and complex modulations. Traditional noise reduction and recognition techniques struggle to maintain performance in low-SNR environments. Recent advances in deep neural networks (DNNs) have enabled progress in both signal enhancement and recognition tasks, but most existing methods remain dependent on time–frequency transform (TFT), increasing computational cost and relying on prior expert knowledge. Besides, these methods rarely provide comprehensive evaluation across multiple metrics. To address these issues, this article proposes LPI-ETDE-STAR, an end-to-end framework for time-domain LPI signal enhancement (LSE) and modulation recognition (LMR). The framework employs an end-to-end time-domain enhancement network (ETDE-Net) to enhance signals, which integrates convolution for local feature extraction and a structured state space (S4) module for capturing long-range dependencies (LRDs). Then, the STAR-MRNet utilizes STAR operations to model high-order and high-dimensional feature interactions for accurate modulation recognition. Comprehensive experiments show that ETDE-Net surpasses both traditional filtering and DNN-based methods for the LSE. Furthermore, STAR-MRNet achieves superior LMR performance based on the outputs of ETDE-Net, outperforming existing DNN approaches under low-SNR conditions.
Chen Cheng, Zhi Sun, Hao-Nan Zhang et al.· IEEE Internet of Things Jour...· 0 citations
The rapiddevelopment of frequency-modulated continuous wave (FMCW) radar has introduced critical mutual interference challenges. Currently, compressed sensing (CS) and deep learning offer promising interference suppression capabilities, while conventional CS implementations face computational bottlenecks and hyperparameter dependence. Meanwhile, the limited interpretability and generalization ability of generic deep networks are also concerns. To address these issues, a learning-based flexible dual-path iterative network (LFDPI-Net) is proposed for suppressing interference between FMCW radars. First, the interference suppression is transformed into model-driven optimization. Second, it combines the interpretability of CS-based methods with feature extraction of deep learning, using a designed mirrored convolutional neural network to perform nonlinear mapping to the target, thereby expanding the receptive field. To enhance generalization, the model flexibly learns hyperparameters in a layered manner. In addition, LFDPI-Net is devised as a dual-path feedforward model to better synchronize the processing of multiple complex-valued pulses. Finally, a multidomain joint constraint term is proposed to stabilize the optimization process by simultaneously considering both target and interference signals. A series of experiments demonstrate that LFDPI-Net can efficiently suppress interference and accurately extract target information, offering a practical solution to mutual interference in dense FMCW radar scenarios.
Hao Zhang, Shunjun Wei, Rui Min et al.· IEEE Transactions on Aerospa...· 0 citations
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