TY - GEN
T1 - Positioning, Navigation, and Timing on the Air
AU - Siraj, Md Sadman
AU - Rahman, Aisha B.
AU - Charatsaris, Panagiotis
AU - Tsiropoulou, Eirini Eleni
AU - Papavassiliou, Symeon
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/6/19
Y1 - 2023/6/19
N2 - The widespread adoption of Positioning, Navigation, and Timing (PNT) services in many applications across modern society, has given rise to the development of various alternative PNT systems, aiming at addressing the Global Positioning System (GPS) vulnerabilities. In this paper, we introduce a collaborative-based PNT solution by jointly exploiting the use of UAV-assisted wireless networks and the collaboration among the targets and the collaborator nodes in the network, who have unknown and a rough estimate of their position and timing, respectively. The minimization problem of the overall system's and each collaborator node's and target's position and timing estimation error is formulated and solved based on the principles of potential games. The existence of a Nash Equilibrium (NE) of the corresponding game is proven, while two different approaches to obtain the game's NE are studied and their tradeoffs are evaluated. The first one follows a game-theoretic approach and the second one is based on the principles of reinforcement learning. A detailed numerical evaluation is performed, via modeling and simulation, in order to demonstrate the benefits and tradeoffs of the proposed PNT solution.
AB - The widespread adoption of Positioning, Navigation, and Timing (PNT) services in many applications across modern society, has given rise to the development of various alternative PNT systems, aiming at addressing the Global Positioning System (GPS) vulnerabilities. In this paper, we introduce a collaborative-based PNT solution by jointly exploiting the use of UAV-assisted wireless networks and the collaboration among the targets and the collaborator nodes in the network, who have unknown and a rough estimate of their position and timing, respectively. The minimization problem of the overall system's and each collaborator node's and target's position and timing estimation error is formulated and solved based on the principles of potential games. The existence of a Nash Equilibrium (NE) of the corresponding game is proven, while two different approaches to obtain the game's NE are studied and their tradeoffs are evaluated. The first one follows a game-theoretic approach and the second one is based on the principles of reinforcement learning. A detailed numerical evaluation is performed, via modeling and simulation, in order to demonstrate the benefits and tradeoffs of the proposed PNT solution.
KW - Game Theory
KW - Navigation
KW - Positioning
KW - Reinforcement Learning
KW - Timing
KW - Unmanned Aerial Vehicles
UR - https://www.scopus.com/pages/publications/85174388443
U2 - 10.1109/DCOSS-IoT58021.2023.00105
DO - 10.1109/DCOSS-IoT58021.2023.00105
M3 - Conference contribution
AN - SCOPUS:85174388443
T3 - Proceedings - 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023
SP - 661
EP - 668
BT - Proceedings - 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023
Y2 - 19 June 2023 through 21 June 2023
ER -