Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145811
Title: Outage bridging and trajectory recovery in visible light positioning using insufficient RSS information
Authors: Zhang, Ran
Liu, Zichuan
Qian, Kemao
Zhang, Sheng
Du, Pengfei
Chen, Chen
Alphones, Arokiaswami
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Zhang, R., Liu, Z., Qian, K., Zhang, S., Du, P., Chen, C., & Alphones, A. (2020). Outage bridging and trajectory recovery in visible light positioning using insufficient RSS information. IEEE Access, 8, 162302-162312. doi:10.1109/ACCESS.2020.3020874
Journal: IEEE Access
Abstract: Indoor positioning technology is vital for various location-aware applications while visible light positioning (VLP) is especially promising due to its ubiquitous and energy-efficient features. VLP has been widely investigated under the assumption of line of sight (LoS), yet, VLP signal blockage can happen frequently in a practical indoor environment and brings about outage problems to indoor localization/tracking services. However, this problem is usually overlooked or sidestepped in the existing works. Our work, for the first time, investigates the outage problem in a received signal strength (RSS)-based VLP system. Efficient algorithms for outage bridging and trajectory recovery are proposed by smartly fusing with insufficient RSS information. Specifically, a partial-RSS-assisted inertial navigation system (PRAINS) inspired by extended Kalman filter (EKF) is developed to bridge sporadic outage, while a bi-directional structured PRAINS (Bid-PRAINS) is developed to use both pre- and post- outage information to recover the lost trajectory information. To further deal with a more general situation when the system noise features are not pre-known and hard to be measured/estimated, a semi-parameterized RNN based learnable Kalman filter (SPR-LKF) is proposed in place of the EKF to learn the observation/transition noise features and optimize the estimation simultaneously through a recurrent neural network (RNN). Extensive tests show that the PRAINS/ Bid-PRAINS has at least 62% accuracy improvement over the conventional inertial navigation system (INS)-only algorithm, while the proposed SPR-LKF/ Bid-SPR-LKF can offer an even better accuracy gain of 70% even without pre-knowing the system noise feature.
URI: https://hdl.handle.net/10356/145811
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.3020874
Rights: © 2020 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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