scholarly journals Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

2016 ◽  
Vol 87 (4) ◽  
pp. 041502 ◽  
Author(s):  
Xinya Li ◽  
Zhiqun Daniel Deng ◽  
Lynn T. Rauchenstein ◽  
Thomas J. Carlson
Keyword(s):  
Source Localization ◽  
Time Difference ◽  
Time Of Arrival ◽  
Time Difference Of Arrival ◽  
Localization Algorithms

Two efficient localization algorithms for multilateration

2009 ◽  
Vol 1 (3) ◽  
pp. 223-229 ◽  
Author(s):  
Mauro Leonardi ◽  
Adolf Mathias ◽  
Gaspare Galati
Keyword(s):  
Simulated Data ◽  
Time Difference ◽  
Wide Area ◽  
Real Case ◽  
Time Of Arrival ◽  
Time Difference Of Arrival ◽  
Overdetermined Systems ◽  
Localization Algorithms ◽  
Spatial Dimensions ◽  
Direct Use

Two localization algorithms for multilateration systems are derived and analyzed. Instead of the classical time difference of arrival (TDOA), a direct use of the time of arrival (TOA) is made. The algorithms work for arbitrary spatial dimensions and overdetermined systems. These derivations are tested in a real-case implementation with simulated data (in particular, the multilateration (MLAT) system installed on the Malpensa Airport in Milan was considered for the MLAT simulation and its possible extension to wide area multilateration (WAM) system was considered for WAM trials). The results are also compared with the present-day algorithms performance, mostly based on TDOA.

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

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3466
Author(s):  
Yuanpeng Chen ◽  
Zhiqiang Yao ◽  
Zheng Peng
Keyword(s):  
Source Localization ◽  
Signal Propagation ◽  
Least Square ◽  
Time Of Arrival ◽  
Propagation Time ◽  
Performance Loss ◽  
Localization Algorithms ◽  
Least Square Problem ◽  
Localization Performance ◽  
Asynchronous State

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.

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