An Efficient Lightweight Network for Intra-pulse Modulation Identification of Low Probability of Intercept Radar Signals

WANG Xudong; WU Jiaxin; CHEN Binbin

doi:10.11999/JEIT240848

Volume 47 Issue 6

Jun. 2025

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WANG Xudong, WU Jiaxin, CHEN Binbin. An Efficient Lightweight Network for Intra-pulse Modulation Identification of Low Probability of Intercept Radar Signals[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1782-1791. doi: 10.11999/JEIT240848

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WANG Xudong, WU Jiaxin, CHEN Binbin. An Efficient Lightweight Network for Intra-pulse Modulation Identification of Low Probability of Intercept Radar Signals[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1782-1791. doi: 10.11999/JEIT240848

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An Efficient Lightweight Network for Intra-pulse Modulation Identification of Low Probability of Intercept Radar Signals

doi: 10.11999/JEIT240848 cstr: 32379.14.JEIT240848

Key Laboratory of Radar Imaging and Microwave Photonics, College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Ministry of Education, Nanjing 210016, China

Funds: The National Natural Science Foundation of China (62271252)

Received Date: 2024-10-09
Rev Recd Date: 2025-03-10

Available Online: 2025-03-25

Publish Date: 2025-06-30

Abstract

Abstract

Objective Low Probability of Intercept (LPI) radar enhances stealth, survivability, and operational efficiency by reducing the likelihood of detection, making it widely used in military applications. However, accurately analyzing the intra-pulse modulation characteristics of LPI radar signals remains a key challenge for radar countermeasure technologies. Traditional methods for identifying radar signal modulation suffer from poor noise resistance, limited applicability, and high misclassification rates. These limitations necessitate more robust approaches capable of handling LPI radar signals under low Signal-to-Noise Ratios (SNRs). This study proposes an advanced deep learning-based method for LPI radar signal recognition, integrating Hybrid Dilated Convolutions (HDC) and attention mechanisms to improve performance in low SNR environments. Methods This study proposes a deep learning-based framework for LPI radar signal modulation recognition. The training dataset includes 12 types of LPI radar signals, including BPSK, Costas, LFM, NLFM, four multi-phase, and four multi-time code signals. To enhance model robustness, a comprehensive preprocessing pipeline is applied. Initially, raw signals undergo SPWVD and CWD time-frequency analysis to generate two-dimensional time-frequency feature maps. These maps are then processed through grayscale conversion, Wiener filtering for denoising, principal component extraction, and adaptive cropping. A dual time-frequency fusion method is subsequently applied, integrating SPWVD and CWD to enhance feature distinguishability (Fig. 2). Based on this preprocessed data, the model employs a modified GhostNet architecture, Dilated CBAM-GhostNet (DCGNet). This architecture integrates HDC and the Convolutional Block Attention Module (CBAM), optimizing efficiency while enhancing the extraction of spatial and channel-wise information (Fig. 7). HDC expands the receptive field, enabling the model to capture long-range dependencies, while CBAM improves feature selection by emphasizing the most relevant spatial and channel-wise features. The combination of HDC and CBAM strengthens feature extraction, improving recognition accuracy and overall model performance. Results and Discussions This study analyzes the effects of different preprocessing methods, network architectures, and computational complexities on LPI radar signal modulation recognition. The results demonstrate that the proposed framework significantly improves recognition accuracy, particularly under low SNR conditions. A comparison of four time-frequency analysis methods shows that SPWVD and CWD achieve higher recognition accuracy (Fig. 8). These datasets are then fused to evaluate the effectiveness of image enhancement techniques. Experimental results indicate that, compared to datasets without image enhancement, the fusion of SPWVD and CWD reduces signal confusion and improves feature discriminability, leading to better recognition performance (Fig. 9). Comparative experiments validate the contributions of HDC and CBAM to recognition performance (Fig. 10). The proposed architecture consistently outperforms three alternative network structures under low SNR conditions, demonstrating the effectiveness of HDC and CBAM in capturing spatial and channel-wise information. Further analysis of three attention mechanisms confirms that CBAM enhances feature extraction by focusing more effectively on relevant time-frequency regions (Fig. 11). To comprehensively evaluate the proposed network, its performance is compared with ResNet50, MobileNetV2, and MobileNetV3 using the SPWVD and CWD fusion-based dataset (Fig. 12). The results show that the proposed network outperforms the other three networks under low SNR conditions, confirming its superior recognition capability for low SNR radar signals. Finally, computational complexity and storage requirements are assessed using floating-point operations and parameter count (Table 2). The results indicate that the proposed network maintains relatively low computational complexity and parameter count, ensuring high efficiency and low computational cost. Overall, the proposed deep learning framework improves radar signal recognition performance while maintaining efficiency. Conclusions This study proposes a deep learning-based method for LPI radar signal modulation recognition using the DCGNet model, which integrates dilated convolutions and attention mechanisms. The framework incorporates an advanced image enhancement preprocessing pipeline, leveraging SPWVD and CWD time-frequency feature fusion to improve feature distinguishability and recognition accuracy, particularly under low SNR conditions. Experimental results confirm that DCGNet outperforms existing methods, demonstrating its practical potential for radar signal recognition. Future research will focus on optimizing the model further and extending its applicability to a wider range of radar signal types and scenarios.

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References(25)

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