TY - GEN
T1 - Towards Effective Swarm-Based GPS Spoofing Detection in Disadvantaged Platforms
AU - Fan, Enguang
AU - Peng, Anfeng
AU - Caesar, Matthew
AU - Kim, Jae
AU - Eckhardt, Josh
AU - Kimberly, Greg
AU - Osipychev, Denis
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85182395103&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85182395103&partnerID=8YFLogxK
U2 - 10.1109/MILCOM58377.2023.10356314
DO - 10.1109/MILCOM58377.2023.10356314
M3 - Conference contribution
AN - SCOPUS:85182395103
T3 - MILCOM 2023 - 2023 IEEE Military Communications Conference: Communications Supporting Military Operations in a Contested Environment
SP - 722
EP - 728
BT - MILCOM 2023 - 2023 IEEE Military Communications Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Military Communications Conference, MILCOM 2023
Y2 - 30 October 2023 through 3 November 2023
ER -