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Sensors 2017, 17(7), 1532; https://doi.org/10.3390/s17071532

GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation

Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
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Received: 19 April 2017 / Revised: 27 June 2017 / Accepted: 28 June 2017 / Published: 30 June 2017
(This article belongs to the Section Remote Sensors)
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Abstract

Spoofing attacks are threatening the global navigation satellite system (GNSS). The maximum likelihood estimation (MLE)-based positioning technique is a direct positioning method originally developed for multipath rejection and weak signal processing. We find this method also has a potential ability for GNSS anti-spoofing since a spoofing attack that misleads the positioning and timing result will cause distortion to the MLE cost function. Based on the method, an estimation-cancellation approach is presented to detect spoofing attacks and recover the navigation solution. A statistic is derived for spoofing detection with the principle of the generalized likelihood ratio test (GLRT). Then, the MLE cost function is decomposed to further validate whether the navigation solution obtained by MLE-based positioning is formed by consistent signals. Both formulae and simulations are provided to evaluate the anti-spoofing performance. Experiments with recordings in real GNSS spoofing scenarios are also performed to validate the practicability of the approach. Results show that the method works even when the code phase differences between the spoofing and authentic signals are much less than one code chip, which can improve the availability of GNSS service greatly under spoofing attacks. View Full-Text
Keywords: GNSS; maximum likelihood estimation; spoofing detection; spoofing mitigation; navigation solution recovery GNSS; maximum likelihood estimation; spoofing detection; spoofing mitigation; navigation solution recovery
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Wang, F.; Li, H.; Lu, M. GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation. Sensors 2017, 17, 1532.

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