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Open AccessArticle

An Innovative Fingerprint Location Algorithm for Indoor Positioning Based on Array Pseudolite

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State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China
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The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(20), 4420; https://doi.org/10.3390/s19204420
Received: 16 August 2019 / Revised: 9 October 2019 / Accepted: 10 October 2019 / Published: 12 October 2019
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Since the signals of the global navigation satellite system (GNSS) are blocked by buildings, accurate positioning cannot be achieved in an indoor environment. Pseudolite can simulate similar outdoor satellite signals and can be used as a stable and reliable positioning signal source in indoor environments. Therefore, it has been proposed as a good substitute and has become a research hotspot in the field of indoor positioning. There are still some problems in the pseudolite positioning field, such as: Integer ambiguity of carrier phase, initial position determination, and low signal coverage. To avoid the limitation of these factors, an indoor positioning system based on fingerprint database matching of homologous array pseudolite is proposed in this paper, which can achieve higher positioning accuracy. The realization of this positioning system mainly includes the offline phase and the online phase. In the offline phase, the carrier phase data in the indoor environment is first collected, and a fingerprint database is established. Then a variational auto-encoding (VAE) network with location information is used to learn the probability distribution characteristics of the carrier phase difference of pseudolite in the latent space to realize feature clustering. Finally, the deep neural network is constructed by using the hidden features learned to further study the mapping relationship between different carrier phases of pseudolite and different indoor locations. In the online phase, the trained model and real-time carrier phases of pseudolite are used to predict the location of the positioning terminal. In this paper, by a large number of experiments, the performance of the pseudolite positioning system is evaluated under dynamic and static conditions. The effectiveness of the algorithm is evaluated by the comparison experiments, the experimental results show that the average positioning accuracy of the positioning system in a real indoor scene is 0.39 m, and the 95% positioning error is less than 0.85 m, which outperforms the traditional fingerprint positioning algorithms. View Full-Text
Keywords: indoor localization; array pseudolite; carrier phase difference; fingerprint matching; deep neural network indoor localization; array pseudolite; carrier phase difference; fingerprint matching; deep neural network
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Huang, L.; Gan, X.; Yu, B.; Zhang, H.; Li, S.; Cheng, J.; Liang, X.; Wang, B. An Innovative Fingerprint Location Algorithm for Indoor Positioning Based on Array Pseudolite. Sensors 2019, 19, 4420.

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