Analysis of the Baseline Data based GPS Spoofing Detection Algorithm
Changhui Jiang, Shuai Chen, Yuwei Chen, Yuming Bo, Boya Zhang, Nanjing University of Science and Technology, China
Alternate Number 3
The location service provided by the Global Navigation Satellite System (GNSS) has changed our life greatly. However, the signal blockage, jamming and spoofing attack lead to a serious impediment to its further application. The receivers are able to be aware of the signal blockage and jamming through the signal processing technology. The goal of the signal blockage and jamming is to prevent the receiver from receiving the broadcast signals and make the receivers fail to provide navigation information. Unlike the signal blockage and jamming, the spoofing attack feeds the receivers false signals and make the receivers to output erroneous navigation information. Thus the spoofing attack detection is more difficult.
Under the circumstance that no spoofing attack existing, each antenna receives unique signals. Consequently, the baseline between each pair of receivers can be calculated. And the length of baseline is assumed to be subject to Gaussian distribution. The mean value is a fixed distance between the pairs of receivers. When the spoofing attack occurs, the antennas receive the identical signal from the spoofing signals which result the position solutions are same. Thus the calculated baseline length becomes to zero. The separation is employed as an indicator to detect the spoofing attack occurrence in the hypothesis test.
In this paper, we analysed the detectors based the baseline data. Three studied cases are investigated with different setups: (1) single fixed baseline; (2) two fixed and independent baselines; (3) Max/Min model for two independent baselines. In addition, the influence of the baseline length on the detection performance is evaluated and analysed, and the performance of three studied cases are evaluated by comparing the receiver operating characteristic (ROC) curves. The results demonstrate effectiveness of the model.