A Multiparameter Spoofing Detection Method Based on Parallel CNN-Transformer Neural Network with Gating Mechanism

ZHUANG Xuebin; NIU Ben; LIN Zijian; ZHANG Linjie

doi:10.11999/JEIT240977

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ZHUANG Xuebin, NIU Ben, LIN Zijian, ZHANG Linjie. A Multiparameter Spoofing Detection Method Based on Parallel CNN-Transformer Neural Network with Gating Mechanism[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240977

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A Multiparameter Spoofing Detection Method Based on Parallel CNN-Transformer Neural Network with Gating Mechanism

doi: 10.11999/JEIT240977

School of Systems Science and Engineering, Sun Yat sen University, Guangzhou 510275, China

Funds: The Aeronautical Science Foundation (202300080M1001)

Received Date: 2024-10-31
Rev Recd Date: 2025-03-13

Available Online: 2025-03-25

Abstract

Abstract

Objective Global Navigation Satellite Systems (GNSS) provide location, velocity, and timing services globally and are widely used. However, their signals are highly susceptible to interference from natural environments or human factors, and existing single-parameter and multi-parameter detection methods have limitations. In an increasingly complex electromagnetic environment, satellite navigation systems face a growing risk of deception and interference. Therefore, it is essential to refine deception interference detection techniques to enhance the generality and adaptability of detection algorithms. This study proposes a multi-parameter deception interference detection algorithm that addresses the limitations of existing methods, ensures the secure and reliable operation of GNSS receivers, and contributes to the safety and stability of satellite navigation systems. Methods Extract key information from the receiver tracking phase. Select five observation metrics: code rate, discriminator result, Doppler shift, carrier-to-noise ratio, and SQM index. Due to the large fluctuations in the original values, apply sliding window processing using Moving Variance (MV) and Moving Mean (MA) to obtain nine feature parameters, forming a multidimensional time series sample. This approach better captures signal feature trends, reduces the effect of data fluctuations, and provides a stable and reliable data foundation for subsequent detection. Construct a Parallel CNN-Transformer Neural network (PCTN) based on a gating mechanism. The network consists of three convolutional neural network modules, eight Transformer encoder modules, and one gating module. The gating mechanism learns the weights of the two branches, fuses their outputs, and detects deception interference signals. Evaluate the model using the TEXBAT dataset and an actual dataset, comparing its performance with five existing algorithms. Results and Discussions The PCTN algorithm performs well on the TEXBAT dataset. As shown in Fig. 6, its classification accuracy for real signals reaches 99.222%, exceeding that of the five comparison algorithms. The ROC curve (Fig. 8) and evaluation metrics (Table 3) indicate that the PCTN algorithm achieves the highest AUC value and outperforms others in accuracy, precision, recall, and F1 score, demonstrating stable classification performance across various deception scenarios and effectively distinguishing deception signals from real signals. A deception interference collection platform collects actual data, and after fine-tuning, the model is tested. The PCTN algorithm maintains significant advantages, achieving the highest AUC value in the ROC curve (Fig. 10). As shown in Table 4, its detection accuracy remains above 94.5%, exceeding other algorithms. Compared with its performance on the TEXBAT dataset, the PCTN algorithm exhibits only a 5% decrease on the actual dataset, significantly lower than other algorithms. This demonstrates its robustness, strong generalization capability, and effectiveness in detecting deception interference in new scenarios. Conclusions This study proposes a multi-parameter deception interference detection algorithm based on Deep Learning (DL). The method extracts multiple parameter features from the receiver tracking stage, forms multidimensional time series samples, and employs the PCTN model for detection. Experimental results demonstrate that, compared with five existing algorithms, the proposed method offers significant advantages. On the TEXBAT dataset, it achieves high accuracy across various deception scenarios. On the actual dataset, it exhibits better generalization performance and effectively differentiates deceptive signals from real signals, even with new datasets. Future research can focus on deploying the algorithm on hardware platforms to enable real-time and accurate deception interference detection in practical satellite navigation scenarios. This will further enhance the security of satellite navigation systems and support the reliable application of satellite navigation technology in complex electromagnetic environments.
- Satellite navigation,
- Deception interference detection,
- Multi parameter,
- Deep Learning (DL)

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References(17)

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