An inertial neural network approach for robust time-of-arrival localization considering clock asynchronization
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NSTL
Elsevier
? 2021 Elsevier LtdThis paper presents an inertial neural network to solve the source localization optimization problem with l1-norm objective function based on the time of arrival (TOA) localization technique. The convergence and stability of the inertial neural network are analyzed by the Lyapunov function method. An inertial neural network iterative approach is further used to find a better solution among the solutions with different inertial parameters. Furthermore, the clock asynchronization is considered in the TOA l1-norm model for more general real applications, and the corresponding inertial neural network iterative approach is addressed. The numerical simulations and real data are both considered in the experiments. In the simulation experiments, the noise contains uncorrelated zero-mean Gaussian noise and uniform distributed outliers. In the real experiments, the data is obtained by using the ultra wide band (UWB) technology hardware modules. Whether or not there is clock asynchronization, the results show that the proposed approach always can find a more accurate source position compared with some of the existing algorithms, which implies that the proposed approach is more effective than the compared ones.
Clock asynchronizationConstrained optimizationInertial neural networkTime of arrival localization
Xu C.、Liu Q.
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School of Cyber Science and Engineering Frontiers Science Center for Mobile Information
School of Mathematics Frontiers Science Center for Mobile Information Communication and Security
NSTL
Elsevier
