A Robust and Fast Method for Spoofing Discrimination and Direction-Finding Based on Short Baseline Rotating Dual Antennas

Global navigation satellite system (GNSS) is under the threat of spoofers that can spoof GNSS timing and positioning results by transmitting fake signals, leading to serious consequences. A lot of anti-spoofing methods have been proposed, among which the spoofing direction-finding methods play an important role in this field. It enables users to locate the spoofers and take the initiative to eliminate these attacks from the source. The antenna arrays are typically utilized in most spoofing direction-finding methods, which are of high complexity in the aspects of both software and hardware. To reduce the system complexity, this paper proposes a method for spoofing direction-finding with the short baseline rotating dual antennas. In the process of spoofing discrimination, a Sinusoidal Detection of the Double Differences (SD3 ) method is proposed to improve the robustness of discrimination. By utilizing the rotating characteristics, the effect of measurement ambiguity and geometric ambiguity is largely eliminated. The theoretical analysis and all-day simulations are conducted to compare the performance between the Dispersion of the Double Differences (D3 ) method and the SD3 method, showing that the SD3 method is more robust with a smaller probability of the Detection Dead Zones (DDZ). In the process of spoofing direction-finding, the antenna baseline attitudes at different times are estimated by utilizing the measurements of authentic signals through the Kalman Filtering (KF) algorithm. And then, the parabolic and linear interpolations are adopted to estimate the location of zero and peak points, further reducing the computational complexity. All of the processes can be completed within a single rotation period, providing real-time protection in GNSS applications. Some theoretical analysis and field tests have been conducted, and it shows that the accuracy of direction-finding is better than 1 degree when the sampling interval is properly selected.