GNSS Spoofing Detection Based on Consistency Check of Velocities

Tao, Huiqi; Wu, Hailing; Li, Hong; Lu, Mingquan

doi:10.1049/cje.2019.01.006

Cited by 6 publications

(2 citation statements)

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“…Single spoofing source interference means that a spoofing source transmits multiple spoofing signals, which is the most common spoofing interference mode at present. In this way, the spoofing signals can be consistent without knowing the target receiver position [12]. Multiple spoofing sources interference means that each spoofing source produces a spoofing signal, and all the spoofing signals are transmitted to the receiver from different directions.…”

Section: Introductionmentioning

confidence: 99%

Anti-Spoofing Method for Improving GNSS Security by Jointly Monitoring Pseudo-Range Difference and Pseudo-Range Sum Sequence Linearity

Zhang,

Liang,

Tian

et al. 2023

Sensors

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Spoofing interference is one of the most emerging threats to the Global Navigation Satellite System (GNSS); therefore, the research on anti-spoofing technology is of great significance to improving the security of GNSS. For single spoofing source interference, all the spoofing signals are broadcast from the same antenna. When the receiver is in motion, the pseudo-range of spoofing signals changes nonlinearly, while the difference between any two pseudo-ranges changes linearly. Authentic signals do not have this characteristic. On this basis, an anti-spoofing method is proposed by jointly monitoring the linearity of the pseudo-range difference (PRD) sequence and pseudo-range sum (PRS) sequence, which transforms the spoofing detection problem into the sequence linearity detection problem. In this paper, the model of PRD and PRS is derived, the hypothesis based on the linearity of PRD sequence and PRS sequence is given, and the detection performance of the method is evaluated. This method uses the sum of squares of errors (SSE) of linear fitting of the PRD sequence and PRS sequence to construct detection statistics, and has low computational complexity. Simulation results show that this method can effectively detect spoofing interference and distinguish spoofing signals from authentic signals.

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Section: Introductionmentioning

confidence: 99%

Anti-Spoofing Method for Improving GNSS Security by Jointly Monitoring Pseudo-Range Difference and Pseudo-Range Sum Sequence Linearity

Zhang,

Liang,

Tian

et al. 2023

Sensors

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show abstract

“…The existence of forwarding spoofing interference seriously threatens the security of navigation and positioning. Therefore, it is necessary to study effective spoofing detection methods according to the characteristics of forwarding spoofing interference [ 3 , 4 , 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Rotating Single-Antenna Spoofing Signal Detection Method Based on IPNN

Chang

Pang

Zhang

et al. 2022

Sensors

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The traditional carrier-phase differential detection technology mainly relies on the spatial processing method, which uses antenna arrays or moving antennas to detect spoofing signals, but it cannot be applied to static single-antenna receivers. Aiming at this problem, this paper proposes a rotating single-antenna spoofing signal detection method based on the improved probabilistic neural network (IPNN). When the receiver antenna rotates at a constant speed, the carrier-phase double difference of the real signal will change with the incident angle of the satellite. According to this feature, the classification and detection of spoofing signals can be realized. Firstly, the rotating single-antenna receiver collects carrier-phase values and performs double-difference processing. Then, we construct an IPNN model, whose smoothing factor can be adaptively adjusted according to the type of failure mode. Finally, we use the IPNN model to realize the classification and processing of the carrier-phase double-difference observations and obtain the deception detection results. In addition, in order to reflect that the method has a certain practical value, we simulate the spoofing scenario of satellite signals and effectively identify abnormal satellite signals according to the detection results of the inter-satellite differential combination. Actual experiments indicate that the detection accuracy of the proposed method for spoofing signals reaches 98.84%, which is significantly better than the classical probabilistic neural network (PNN) and back-propagation neural network (BPNN), and the method can be implemented in fixed base station receivers for the real-time detection of forwarding spoofing.

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