A Novel Method for Asynchronous Time-of-Arrival-Based Source Localization: Algorithms, Performance and Complexity

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 12; p. 3466
• Main Authors: Chen, Yuanpeng, Yao, Zhiqiang, Peng, Zheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 19.06.2020; MDPI
• Subjects: Accuracy; Algorithms; asynchronous sensor networks; Estimates; Global positioning systems; GPS; Inventory control; Localization; Noise; Sensors; Signal processing; source localization; time-of-arrival
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20123466

In time-of-arrival (TOA)-based source localization, accurate positioning can be achieved only when the correct signal propagation time between the source and the sensors is obtained. In practice, a clock error usually exists between the nodes causing the source and sensors to often be in an asynchronous state. This leads to the asynchronous source localization problem which is then formulated to a least square problem with nonconvex and nonsmooth objective function. The state-of-the-art algorithms need to relax the original problem to convex programming, such as semidefinite programming (SDP), which results in performance loss. In this paper, unlike the existing approaches, we propose a proximal alternating minimization positioning (PAMP) method, which minimizes the original function without relaxation. Utilizing the biconvex property of original asynchronous problem, the method divides it into two subproblems: the clock offset subproblem and the synchronous source localization subproblem. For the former we derive a global solution, whereas the later is solved by a proposed efficient subgradient algorithm extended from the simulated annealing-based Barzilai–Borwein algorithm. The proposed method obtains preferable localization performance with lower computational complexity. The convergence of our method in Lyapunov framework is also established. Simulation results demonstrate that the performance of PAMP method can be close to the optimality benchmark of Cramér–Rao Lower Bound.