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Robust Localization for Mixed LOS/NLOS Environments With Anchor Uncertainties

Li, Y; Ma, S; Yang, G; Wong, KK; (2020) Robust Localization for Mixed LOS/NLOS Environments With Anchor Uncertainties. IEEE Transactions on Communications , 68 (7) pp. 4507-4521. 10.1109/TCOMM.2020.2982633. Green open access

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Abstract

Localization is particularly challenging when the environment has mixed line-of-sight (LOS) and non-LOS paths and even more challenging if the anchors’ positions are also uncertain. In the situations in which the parameters of the LOS-NLOS propagation error model and the channel states are unknown and uncertainties for the anchors exist, the likelihood function of a localizing node is computationally intractable. In this paper, assuming the knowledge of the prior distributions of the error model parameters and that of the channel states, we formulate the localization problem as the maximization problem of the posterior distribution of the localizing node. Then we apply variational distributions and importance sampling to approximate the true posterior distributions and estimate the target’s location using an asymptotic minimum mean-square-error (MMSE) estimator. Furthermore, we analyze the convergence and complexity of the proposed variational Bayesian localization (VBL) algorithm. Computer simulation results demonstrate that the proposed algorithm can approach the performance of the Bayesian Cramer-Rao bound (BCRB) and outperforms conventional algorithms

Type: Article
Title: Robust Localization for Mixed LOS/NLOS Environments With Anchor Uncertainties
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TCOMM.2020.2982633
Publisher version: https://doi.org/10.1109/TCOMM.2020.2982633
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Bayesian Cramer-Rao bound (BCRB), variational Bayesian localization (VBL), mixed LOS/NLOS measurements, anchor node uncertainties, asymptotic minimum mean square error (MMSE)
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10106698
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