A novel adaptive radio map for RSS‐based indoor positioning. (24th April 2018)

• Record Type: Journal Article
• Authors: Ye, Ayong; Yang, Xiaoliang; Li, Qing; Chen, Aimin
• Other Names: Chen Jinjun guestEditor.; Zheng Xianghan guestEditor.; Rong Chunming guestEditor.; Badarch Tuyatsetseg guestEditor.; Nanda Priyadarsi guestEditor.; Puthal Deepak guestEditor.; Mohanty Saraju P. guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 23(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-04-24
• Subjects: fingerprinting positioning -- indoor positioning -- radio map updating -- received signal strength; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4486
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Fingerprinting positioning is used for indoor location estimation because of low cost and open access properties, in which a radio map is built by calibrating signal‐strength values at several training locations in the offline phase. However, due to the sophisticated propagation of radio signals, the received signal strength (RSS) in wireless network change as the environment changes, and the radio map built in the offline phase may be out of date. Furthermore, the recalibration of signal‐strength values for each environment change is laborious and time consuming. In this paper, we present a novel algorithm to reconstruct a radio map using real‐time signal‐strength readings received at some reference points. We first demonstrate that different features of signal propagation are obtained in different regions of indoor environment. Then, the indoor environment is divided into several regions by clustering the path‐loss parameters of each reference point. In addition, the relationship between the RSS of reference points and calibration nodes is established with robust linear regression. Finally, the real‐time radio map is updated dynamically according to robust regression and the real‐time RSSs of the calibration nodes. The experimental results show the usefulness of the proposed method and the accuracy of the localization can be improved.