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View all- Lin DChen C(2022)PSO-Adam – A Case Study of Mobile Positioning2022 IEEE 11th Global Conference on Consumer Electronics (GCCE)10.1109/GCCE56475.2022.10014399(685-686)Online publication date: 18-Oct-2022
Mobile Positioning Based on TAE-GRU
Authors: 
Canyang Guo, Ling Wu, Cheng Shi, Chi-Hua ChenAuthors Info & Claims
Canyang Guo, Ling Wu, Cheng Shi, Chi-Hua ChenAuthors Info & ClaimsPages 97 - 104
Abstract
This paper motivates to solve the multiple mapping of Received Signal Strength Indications (RSSIs) and location estimating problem in mobile positioning. A mobile positioning method based on Time-distributed Auto Encoder and Gated Recurrent Unit (TAE-GRU) is proposed to realize the mobile positioning. To distinguish the identical RSSI of different temporal steps, this paper develops a reconstructed model based on Time-distributed Auto Encoder (TAE), which is conducive for further learning of the estimated model. Among them, time-distributed technology is utilized to translate the data of each temporal step separately accommodating the temporal characteristics of RSSI data. Besides, an estimated model based on Gated Recurrent Unit (GRU) is developed to learn the temporal relationship of RSSI data to estimate the locations of mobile devices. Combining the TAE model and GRU model, the proposed model is provided with the capability of solving multiple mapping and mobile positioning dilemma. Massive experimental results demonstrated that the proposed method provides superior performance than comparative methods when solving multiple mapping and positioning problems.
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- Mobile Positioning Based on TAE-GRU
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Published In
April 2021
726 pages
ISBN:9781450383134
DOI:10.1145/3442442
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Published: 03 June 2021
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View all- Lin DChen C(2022)PSO-Adam – A Case Study of Mobile Positioning2022 IEEE 11th Global Conference on Consumer Electronics (GCCE)10.1109/GCCE56475.2022.10014399(685-686)Online publication date: 18-Oct-2022
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