• Journal of Positioning, Navigation, and Timing
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• v.10 no.3
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• pp.169-177
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• 2021

In this paper, we propose a novel global navigation satellite system (GNSS) spoofing detection technique through an array antenna-based direction of arrival (DoA) estimation of satellite and spoofer. Specifically, we consider a sophisticated GNSS spoofing attack scenario where the spoofer can accurately mimic the multiple pseudo-random number (PRN) signals since the spoofer has its own GNSS receiver and knows the location of the target receiver in advance. The target GNSS receiver precisely estimates the DoA of all PRN signals using compressed sensing-based orthogonal matching pursuit (OMP) even with a small number of samples, and it performs spoofing detection from the DoA estimation results of all PRN signals. In addition, considering the initial situation of a sophisticated spoofing attack scenario, we designed the algorithm to have high spoofing detection performance regardless of the relative spoofing signal power. Therefore, we do not consider the assumption in which the power of the spoofing signal is about 3 dB greater than that of the authentic signal. Then, we introduce design parameters to get high true detection probability and low false alarm probability in tandem by considering the condition for the presence of signal sources and the proximity of the DoA between authentic signals. Through computer simulations, we compare the DoA estimation performance between the conventional signal direction estimation method and the OMP algorithm in few samples. Finally, we show in the sophisticated spoofing attack scenario that the proposed spoofing detection technique using OMP-based estimated DoA of all PRN signals outperforms the conventional spoofing detection scheme in terms of true detection and false alarm probability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5A
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• pp.717-725
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• 2000

Within the last several years, there has been a large number of algorithms developed for face recognition. The majority of these algorithms have been view- and projection-based algorithms. Our definition of projection is not restricted to projecting the image onto an orthogonal basis the definition is expansive and includes a general class of linear transformation of the image pixel values. The class includes correlation, principal component analysis, clustering, gray scale projection, and matching pursuit filters. In this paper, we perform a detailed analysis of this class of algorithms by evaluating them on the FERET database of facial images. In our experiments, a projection-based algorithms consists of three steps. The first step is done off-line and determines the new basis for the images. The bases is either set by the algorithm designer or is learned from a training set. The last two steps are on-line and perform the recognition. The second step projects an image onto the new basis and the third step recognizes a face in an with a nearest neighbor classifier. The classification is performed in the projection space. Most evaluation methods report algorithm performance on a single gallery. This does not fully capture algorithm performance. In our study, we construct set of independent galleries. This allows us to see how individual algorithm performance varies over different galleries. In addition, we report on the relative performance of the algorithms over the different galleries.