Non-stationary Interference Mitigation for GNSS Using Sparse Representation

Ren, Yuetao; Zhi, Yongfeng; Gao, Haijing; Zhang, Jun

doi:10.1007/978-981-19-6613-2_72

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…A supplementary point of consideration within this context is that numerous blanket jamming belong to nonstationary, time-varying signals [18]. Simple time-domain or frequency-domain transform methods cannot accurately analyze such signals as their frequency content changes over time.…”

Section: Introductionmentioning

confidence: 99%

GNSS blanket jamming classification algorithm based on spatial attention mechanism and residual shrinkage neural network

Lin,

Zhuang,

Niu

et al. 2024

Meas. Sci. Technol.

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In the context of an increasingly complex electromagnetic environment, satellite navigation systems have become highly susceptible to jamming. Detecting and classifying jamming has thus become crucial for taking effective anti-jamming measures. This paper addresses the issue that the classification accuracy of blanket jamming declines drastically in low jamming-to-noise ratio (JNR) scenarios. To tackle this challenge, a novel algorithm is proposed that combines the spatial attention mechanism with a residual shrinkage neural network (RSN-SA) to classify 10 types of blanket jamming, ranging from single jamming to convolutional compound jamming. Specifically, the proposed algorithm first employs the Fourier Synchrosqueezed Transform (FSST) to extract time-frequency (TF) domain features from the original jamming signal, generating corresponding TF images. Then, the RSN-SA is employed to identify and classify these images effectively while minimizing the impact of noise-related features. This allows the main parts of the TF images to be focused on, resulting in higher recognition accuracy. Simulation results demonstrate that RSN-SA achieves close to 100% accuracy for six single blanket jamming signals. Moreover, compared with the other five algorithms, RSN-SA effectively enhances the classification accuracy of convolutional compound jamming signals in low JNR scenarios and improves the recognition stability in high JNR scenarios. Overall, the proposed algorithm provides a promising solution for classifying blanket jamming in satellite navigation systems with high accuracy and robustness.

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Section: Introductionmentioning

confidence: 99%