DOA Classification and CCPM-PC based GNSS Spoofing Detection Technique

Guanghui Xu, Feng Shen, Moeness Amin, Chun Wang

Abstract:	We propose a spoofing detection technique that predicates on multiple sources of information and parameter estimation. The proposed technique is divided into two consecutive processes. In the first state, direction-of-arrival (DOA) estimation is performed and coordinate system transformation is employed for categorization of emitters and selection of those of high elevation angles. Beamspace, in lieu of sensor space, is applied to provide high gain in the spatial sector of interest and place low-sensitivity in the sector covering low-elevation angles. The GNSS signals self-coherence property is used to remove, or at least significantly reduce, noise components in the data covariance matrix. Due to strong correlation between the spoofing signal and GNSS authentic signal, rank recovery technique is applied to the reduced noise covariance matrix. In the second stage, the four strongest signals are chosen for power comparison and cross-correlations. Each individual signal can be separated from the received data by using oblique projection. The united complementary power comparison and cross-correlation results offer a reliable way for the detection of spoofing signal. Then a DOA estimation and elevation angle classification, cross-correlation peak monitoring, and power calculation and comparison (CCPM-PC) based spoofing detection technique is developed in this paper. Simulation results show that the proposed method can detect the spoofing signals effectively.
Published in:	2018 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 23 - 26, 2018 Hyatt Regency Hotel Monterey, CA
Pages:	389 - 396
Cite this article:	Xu, Guanghui, Shen, Feng, Amin, Moeness, Wang, Chun, "DOA Classification and CCPM-PC based GNSS Spoofing Detection Technique," 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, April 2018, pp. 389-396. https://doi.org/10.1109/PLANS.2018.8373405
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