Towards Effective Swarm-Based GPS Spoofing Detection in Disadvantaged Platforms

Modern battlefields are subject to spoofing of GPS signals. While large aircraft platforms can counter the effects of GPS spoofing via redundant and dissimilar sensors, the disadvantaged nodes with smaller platforms such as Air Launched Effects (ALE), with more limited capabilities, can be vulnerable. That said, other sensors on the platform may give clues to the drone about where it is located. In this paper, we investigate the ability of sensor fusion to remediate spoofing of GPS signals for ALE platforms. We first conduct performance comparison among several complementary techniques, including the use of inertial measurement units (IMUs), communication with nearby ALEs (to compare GPS readings), and received signal strength from networking connections (to estimate distance to neighboring ALEs, etc.) We then propose a novel architecture that performs sensor fusion to intelligently combine observations across multiple sensors so as to maximize the ability to detect GPS spoofing as well as to reconstruct coordinates with confidence levels. From a simulation study based on real-world mobility and sensor traces, we find that our approach can improve location estimates accuracy by multiple orders of magnitude as compared to simple baseline techniques, supplementing the ability for ALEs to navigate and execute missions in GPS-denied environments.