TOA-based indoor localization and tracking with inaccurate floor plan map via MRMSC-PHD filter

Zhang, H., Tan, S. Y. and Seow, C. K. (2019) TOA-based indoor localization and tracking with inaccurate floor plan map via MRMSC-PHD filter. IEEE Sensors Journal, 19(21), pp. 9869-9882. (doi:10.1109/JSEN.2019.2926433)

200579.pdf - Accepted Version



This paper proposes a novel indoor localization scheme to jointly track a mobile device (MD) and update an inaccurate floor plan map using the time-of-arrival measured at multiple reference devices (RDs). By modeling the floor plan map as a collection of map features, the map and MD position can be jointly estimated via a multi-RD single-cluster probability hypothesis density (MSC-PHD) filter. Conventional MSC-PHD filters assume that each map feature generates at most one measurement for each RD. If single reflections of the detected signal are considered as measurements generated by map features, then higher-order reflections, which also carry information on the MD and map features, must be treated as clutter. The proposed scheme incorporates multiple reflections by treating them as virtual single reflections reflected from inaccurate map features and traces them to the corresponding virtual RDs (VRDs), referred to as a multi-reflection-incorporating MSC-PHD (MRMSC-PHD) filter. The complexity of using multiple reflection paths arises from the inaccuracy of the VRD location due to inaccuracy in the map features. Numerical results show that these multiple reflection paths can be modeled statistically as a Gaussian distribution. A computationally tractable implementation combining a new greedy partitioning scheme and a particle-Gaussian mixture filter is presented. A novel mapping error metric is then proposed to evaluate the estimated map's accuracy for plane surfaces. Simulation and experimental results show that our proposed MRMSC-PHD filter outperforms the existing MSC-PHD filters by up to 95% in terms of average localization and by up to 90% in terms of mapping accuracy.

Item Type:Articles
Keywords:Localization, tracking, time-of-arrival, multi-reflections, multi-RD single-cluster, probability hypothesis density filter, mapping error.
Glasgow Author(s) Enlighten ID:Seow, Dr Chee Kiat
Authors: Zhang, H., Tan, S. Y., and Seow, C. K.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:IEEE Sensors Journal
ISSN (Online):1558-1748
Published Online:02 July 2019
Copyright Holders:Copyright © 2019 IEEE
First Published:First published in IEEE Sensors Journal 19(21): 9869-9882
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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