Single-Channel High-Precision Sparse DOA Estimation of GNSS Signals for Deception Suppression

KANG Weiquan; LU Zukun; LI Baiyu; SONG Jie; XIAO Wei

doi:10.11999/JEIT250725

Volume 47 Issue 12

Dec. 2026

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(12): 4917-4925

KANG Weiquan, LU Zukun, LI Baiyu, SONG Jie, XIAO Wei. Single-Channel High-Precision Sparse DOA Estimation of GNSS Signals for Deception Suppression[J]. Journal of Electronics & Information Technology, 2025, 47(12): 4917-4925. doi: 10.11999/JEIT250725

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KANG Weiquan, LU Zukun, LI Baiyu, SONG Jie, XIAO Wei. Single-Channel High-Precision Sparse DOA Estimation of GNSS Signals for Deception Suppression[J]. Journal of Electronics & Information Technology, 2025, 47(12): 4917-4925. doi: 10.11999/JEIT250725

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Single-Channel High-Precision Sparse DOA Estimation of GNSS Signals for Deception Suppression

doi: 10.11999/JEIT250725 cstr: 32379.14.JEIT250725

Key Laboratory of Satellite Navigation Technology, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds: The National Natural Science Foundation of China (62003354, U20A20193)

Received Date: 2025-08-07
Accepted Date: 2025-12-01
Rev Recd Date: 2025-12-01

Available Online: 2025-12-05

Publish Date: 2025-12-10

Abstract

Abstract

Objective Spoofing attacks present a major threat to the reliability and security of Global Navigation Satellite System (GNSS) receivers used in civilian and military navigation. Conventional anti-spoofing approaches based on multi-antenna arrays require substantial hardware resources and show reduced estimation accuracy under low Signal-to-Noise Ratio (SNR) conditions, which limits their suitability for constrained or adverse environments. This study proposes a single-channel high-precision sparse Direction-of-Arrival (DOA) estimation method designed to suppress spoofing signals in GNSS receivers. The aim is to reduce hardware complexity and achieve accurate DOA estimation in very low SNR conditions. By using the spatial sparsity of GNSS signals and integrating advanced signal-processing techniques, the method provides a cost-efficient and robust approach for strengthening GNSS resilience against deceptive interference. Methods The proposed method uses a single-channel processing framework to estimate the DOA of GNSS signals with high precision through a multi-step strategy designed for spoofing suppression. The process begins by reconstructing the digital intermediate-frequency signal using tracking-loop parameters such as code phase and carrier Doppler obtained from a reference array element. This reconstruction uses the orthogonality of pseudo-random noise codes in GNSS signals and enables correlation between the reconstructed signal and the original array data to improve the SNR before despreading. This step isolates a clean steering vector and reduces noise and interference. The method then uses the spatial sparsity of GNSS signals, which results from the limited number of authentic satellites and potential spoofing sources in the angular domain. An overcomplete dictionary is formed from steering vectors corresponding to a grid of candidate azimuth and elevation angles. The DOA estimation is expressed as a sparse reconstruction problem in which the steering vector is represented as a sparse combination of dictionary elements. To solve this efficiently, the Alternating Direction Method of Multipliers (ADMM) is used to iteratively optimize a regularized objective that balances data fidelity and sparsity. A two-stage grid-refinement process, beginning with a coarse search followed by a finer resolution, reduces computational cost while preserving accuracy. After DOA estimates are obtained, spoofing signals are identified based on their angular proximity to authentic signals, and a Linearly Constrained Minimum Variance (LCMV) beamformer is applied to suppress these interferers while retaining legitimate signals. Results and Discussions Simulations were performed to evaluate the proposed method under a range of low SNR conditions using a 4×4 uniform planar array and Beidou B3I signals as the test case. The results show that the single-channel sparse DOA estimation method provides markedly higher accuracy and resolution than Unitary ESPRIT and Cyclic MUSIC. When the SNR is –35 dB, the method achieves Root Mean Square Errors (RMSE) for azimuth and elevation estimates below 1 degree (Fig. 2), whereas the benchmark methods yield errors greater than 30 degrees. The method also resolves signals with angular separations as small as 1 degree (Fig. 4(a), Fig. 4(b)), demonstrating its strong resolution capability. Using the accurate DOA estimates derived from the proposed method, LCMV beamforming then suppresses spoofing signals effectively. As shown in Fig. 5(b), the high-fidelity DOA estimates enable the beamformer to place deep nulls at the spoofing directions (for example, (10°, 250°) and (20°, 250°)) and to attenuate spoofers while retaining authentic signals. In contrast, the reduced DOA accuracy of Cyclic MUSIC (Fig. 5(a)) leads to misaligned nulls and weaker suppression. These results confirm the practical value of accurate DOA estimation for robust spoofing mitigation. Conclusions This study presents a single-channel high-precision sparse DOA estimation method for GNSS spoofing suppression, addressing the limitations of conventional multi-antenna techniques related to hardware complexity and reduced performance under low-SNR conditions. By combining signal reconstruction, sparse modeling, and ADMM-based optimization, the method provides accurate and high-resolution DOA estimation in challenging environments, with simulations showing RMSE below 1 degree at –35 dB SNR. When used with LCMV beamforming, it suppresses spoofing signals effectively and improves GNSS reliability while requiring minimal hardware resources. This cost-efficient approach is well suited to applications with limited system capacity, as it reduces reliance on complex array configurations and maintains strong security performance. Future work may examine its performance in dynamic settings such as moving spoofers or multipath conditions, as well as its integration with other anti-spoofing strategies. This research offers a practical and high-performance framework for strengthening GNSS systems and has clear value for navigation safety and operational stability.
- Satellite navigation,
- Spoofing detection,
- Direction of Arrival (DOA) estimation,
- Sparse reconstruction,
- Alternating Direction Method of Multipliers (ADMM)

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References(17)

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