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作者:

Yan, Hairong (Yan, Hairong.) | Peng, Tingqing (Peng, Tingqing.) | Liu, Honggang (Liu, Honggang.) | Ding, Yuxia (Ding, Yuxia.)

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EI Scopus

摘要:

With the development of AI and industrial technology, indoor positioning has gradually attracted wide attention. General solution in the industrial positioning field has not yet be found with current positioning technology. The problem of industrial intelligent positioning technology is worth studying. Due to the extensive deployment of Wi-Fi, this paper will apply the improved intelligent fingerprint positioning technology to the field of robot positioning. At present, most fingerprint positioning methods need to collect a large amount of data, and field investigation requires a lot of time and manpower, which complicates the positioning method. This paper puts forward the idea of synchronization while collecting and locating. By collecting wifi-list and processed the information into fingerprint information, the fingerprint database is updated regularly in the meanwhile to eliminate fingerprint noise and reduce the dependence of location on environment. The improved Gauss filtering method is used to test the collected fingerprint data to increase the reliability of fingerprint data. An effective matching method TNN is proposed which reduce redundant information in fingerprint database, improve the speed and accuracy of location by adjusting fingerprint information. After testing, the accuracy of the fingerprint positioning method proposed in this study reaches 2 meters, and the average accuracy of fingerprint data in the accuracy range is 98.5%, and what's more, it is easy to deploy and implement. © 2019 IEEE.

关键词:

Artificial intelligence Indoor positioning systems Industry Intelligent robots Location Wireless local area networks (WLAN)

作者机构:

  • [ 1 ] [Yan, Hairong]Information Faculty, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Peng, Tingqing]Information Faculty, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Liu, Honggang]School of Computer Science, Fudan University, Shanghai; 200433, China
  • [ 4 ] [Ding, Yuxia]School of Computer Science, Fudan University, Shanghai; 200433, China

通讯作者信息:

  • [peng, tingqing]information faculty, beijing university of technology, beijing; 100124, china

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年份: 2019

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 6

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