An inertial neural network approach for robust time-of-arrival localization considering clock asynchronization. (February 2022)

• Record Type: Journal Article
• Authors: Xu, Chentao; Liu, Qingshan
• Is Part Of: Neural networks. Volume 146(2022)
• Page Start: 98
• Page End: 106
• Publication Date: 2022-02
• Subjects: Time of arrival localization -- Clock asynchronization -- Inertial neural network -- Constrained optimization; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2021.11.012
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: This paper presents an inertial neural network to solve the source localization optimization problem with l 1 -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 l 1 -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.