GNSS Spoofing Detection and Mitigation based on the Cyclic MUSIC Algorithm

Jiaqi Zhang, Xiaowei Cui, Chenxi Peng, Mingquan Lu

Abstract: In recent research, spatial processing technique based on antenna array has been considered as one of the most effective anti-spoofing methods. This paper provides a novel spoofing suppression scheme based on the Cyclic Multiple Signal Classification (MUSIC) algorithm. In this method, the cyclostationarity of navigation signals are fully excavated to estimate the spatial power spectrum before the despreading process of the receiver. Then, based on the assumption that the spoofing signals with different spurious pseudo random noise (PRN) codes come from one single-antenna source, the spoofing attack can be detected if the power of a certain direction is significantly larger than other directions. Finally, the subspace projection is adopted to eliminate the spoofing signals and beamforming for each satellite ensures that the power of the authentic signals is not attenuated. The proposed technique has lower computational complexity than the post-despreading methods because all the operations are performed on the digitized baseband samples without any modification to the receiver. In addition, different with other pre-despreading methods, this method can accurately estimate the DOAs of the spoofing and authentic signals, which can not only provide higher gain for the authentic signals but also be helpful for the interference source positioning in some applications.
Published in: Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)
September 16 - 20, 2019
Hyatt Regency Miami
Miami, Florida
Pages: 3215 - 3229
Cite this article: Zhang, Jiaqi, Cui, Xiaowei, Peng, Chenxi, Lu, Mingquan, "GNSS Spoofing Detection and Mitigation based on the Cyclic MUSIC Algorithm," Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), Miami, Florida, September 2019, pp. 3215-3229.
https://doi.org/10.33012/2019.17063
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