Radar Emitter Individual Identification Based on Information Sidebands of Unintentional Phase Modulation on Pulses

HUANG Xiangsong; WANG Zhen; PAN Dapeng; ZHAO Yiyang

doi:10.11999/JEIT240774

Volume 47 Issue 6

Jun. 2025

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HUANG Xiangsong, WANG Zhen, PAN Dapeng, ZHAO Yiyang. Radar Emitter Individual Identification Based on Information Sidebands of Unintentional Phase Modulation on Pulses[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1762-1771. doi: 10.11999/JEIT240774

Citation:

HUANG Xiangsong, WANG Zhen, PAN Dapeng, ZHAO Yiyang. Radar Emitter Individual Identification Based on Information Sidebands of Unintentional Phase Modulation on Pulses[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1762-1771. doi: 10.11999/JEIT240774

Citation:

HUANG Xiangsong, WANG Zhen, PAN Dapeng, ZHAO Yiyang. Radar Emitter Individual Identification Based on Information Sidebands of Unintentional Phase Modulation on Pulses[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1762-1771. doi: 10.11999/JEIT240774

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Radar Emitter Individual Identification Based on Information Sidebands of Unintentional Phase Modulation on Pulses

doi: 10.11999/JEIT240774 cstr: 32379.14.JEIT240774

HUANG Xiangsong^{1, 2, 3},
WANG Zhen¹,
PAN Dapeng^{1, 2, 3
,
,},
ZHAO Yiyang¹

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Advanced Ship Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China
3.
AVIC United Technology Center for Electromagnetic Spectrum Collaborative Detection and Intelligent cognition, Harbin Engineering University, Harbin 150001, China

Funds: Heilongjiang Province Education Science Planning Project (GJC1319018), Harbin Engineering University School-level Undergraduate Education and Teaching Reform Research Project (JG2019B24, JG2019B86)

Received Date: 2024-09-09
Rev Recd Date: 2025-05-16

Available Online: 2025-05-31

Publish Date: 2025-06-30

Abstract

Abstract

Objective Radar Emitter Identification (REI) plays a critical role in complex battlefield environments and electronic warfare. Conventional methods for extracting Unintentional Phase Modulation On Pulse (UPMOP) features are often ineffective at distinguishing emitters of the same model and manufacturer, due to hardware similarities. This limitation hinders the ability to accurately identify individual emitters. To overcome this challenge, a new method is proposed that integrates Information Sidebands Of Unintentional Phase Modulation On Pulses (ISOUPMOP) with deep learning techniques. This approach mitigates phase ambiguity and noise, thereby enhancing the ability to discriminate between emitters of the same type. A Dual-Loop Circular DILated Convolutional Neural Network (DLCDIL-CNN) is used to expand the receptive field, improving the processing of long-sequence data. This design results in a more accurate identification of radar radiation sources with similar hardware characteristics. Methods Unintentional phase modulation refers to subtle fluctuations in the phase of radar signals, primarily caused by hardware instability and nonlinear characteristics of radar transmitters. This study first applies Variational Mode Decomposition (VMD) to denoise the UPMOP features by decomposing them into signal components at different frequency bands. These components are then analyzed using the Wavelet SynchroSqueezed Transform (WSST) to perform high-resolution time-frequency analysis and extract phase information from multiple time-frequency components. The mean of the time-frequency ridge line is used for discriminative analysis, and components containing ISOUPMOP are reconstructed as identification features. For individual identification, a DLCDIL-CNN is employed. The input data are split into two branches, each processed using Circular Dilated Convolution (CDC) layers with ring padding. This architecture expands the receptive field without introducing boundary effects, enabling the model to capture long-range dependencies and maintain robustness to data shifts. Results and Discussions Visualization experiments reveal that traditional UPMOP features are strongly influenced by the emitter model, resulting in poor differentiation among sources of the same type (Figure 9). Feature map analysis and model performance comparisons show that HeatMap visualizations of the fully connected layers highlight higher Activation Values (AV) in the final layer of the DLCDIL-CNN compared to those of CDIL-CNN and conventional CNNs (Figure 11). This indicates that the CDC used in DLCDIL-CNN is more effective at capturing global features. Robustness validation is conducted under varying Signal-to-Noise Ratio (SNR) conditions, comparing the proposed method with several other identification approaches. At 5 dB SNR, the ISOUPMOP feature extraction method combined with ResNet1D achieves an average identification accuracy of 66.17%, outperforming other methods. When paired with the DLCDIL-CNN, average accuracy increases to 87.58%, representing a 21.42% improvement over ResNet1D (Figure 12). Although DLCDIL-based identification using noisy UPMOP features remains functional, its accuracy is lower than that of the proposed approach. Moreover, smoothed UPMOP features fail to support accurate recognition, even under high SNR conditions. These results suggest that although noisy UPMOP retains individual information, emitters of the same model exhibit similar circuit behavior, and denoising may remove critical nonlinear characteristics. As the number of individual emitters increases, the smoothed UPMOP feature curve leads to feature aliasing, reducing recognition performance. In contrast, the ISOUPMOP method retains unintentional signal characteristics while eliminating trend components, thereby enhancing model generalizability and mitigating overfitting. Conclusions To improve individual differentiation among radar sources of the same model, this study proposes a method that combines ISOUPMOP feature extraction with a DLCDIL-CNN architecture. The approach enhances feature discriminability by preserving subtle individual variations and improves identification accuracy through expanded receptive fields and reduced boundary effects. Experimental results confirm that the proposed method achieves an average accuracy of 87.58% under 5 dB SNR for 10 emitters of the same model. These findings indicate that the method effectively resolves the challenge of limited individual differentiation in traditional UPMOP-based techniques and provides a reliable framework for radar emitter individual identification.

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References(20)

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