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Author:

Deng, Wanghua (Deng, Wanghua.) | Huang, Zhangqin (Huang, Zhangqin.) (Scholars:黄樟钦)

Indexed by:

EI Scopus

Abstract:

Due to the small size and no battery needed, passive radio frequency identification(RFID) tag is easily integrated to everything. It is useful on item-level location scenarios like finding an important document at home. Received signal strength indicator(RSSI) based fingerprint indoor positioning methodology is not only appropriate for the active short range radio frequency(RF) system, but also suitable for passive RFID system. According to our repeated experiments, we find out that although a tag's received signal strength(RSS) values distribution against a fixed position doesn't conform to Gaussian process, the value probability distributions is always similar in the nearby positions. A fingerprint-like solution by using RSS value probability distribution comparison for passive RFID system is presented in this paper. The first phase is to build a probability distribution fingerprint database for the targeted area, while the second phase is the real-time probability comparison and location determination. Based on our experimental result, the new solution using our probability distribution comparison method performs higher positioning accuracy than the existing approach. © 2017 IEEE.

Keyword:

Mobile computing Distributed database systems Probability Indoor positioning systems Probability distributions Radio frequency identification (RFID) Radio waves

Author Community:

  • [ 1 ] [Deng, Wanghua]Beijing Advanced Innovation Center for Future Internet Technology, Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing, China
  • [ 2 ] [Huang, Zhangqin]Beijing Advanced Innovation Center for Future Internet Technology, Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing, China

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Source :

Year: 2017

Volume: 2018-January

Page: 905-909

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 7

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