Journal article
A low-cost indoor localization system based on received signal strength indicator by modifying trilateration for harsh environments
International journal of distributed sensor networks, Vol.14(6), p.155014771877968
06/01/2018
DOI: 10.1177/1550147718779680
Abstract
Indoor localization systems using received signal strength indicator are very popular for their low power and low complexity, but some drawbacks limit their accuracy, especially in harsh environments, such as multipath and fluctuation. Most existing approaches solve the problem by fingerprinting. However, fingerprinting based algorithms are unsuitable for changeable environments like construction, since they all demand prior knowledge of the environment. This article studies a novel localization system to achieve an acceptable accuracy position using received signal strength indicator for harsh environments like construction. Based on analysis of the targets' behavior pattern, we first use curve fitting to filter the distance derived from received signal strength indicator. And then, we propose a distance ratio location algorithm to estimate the targets' positions. Furthermore, Kalman filter is considered to smooth the position results. This method has been applied in the Monitoring and Control System for Underground Tunneling Based on Cyber Physical System Project in Wuhan for tracking workers and vehicles. Practice results show that our system has an acceptable accuracy.
Details
- Title: Subtitle
- A low-cost indoor localization system based on received signal strength indicator by modifying trilateration for harsh environments
- Creators
- Haibin Tong - Northeastern UniversityQingxu Deng - Northeastern UniversityTianyu Zhang - Northeastern UniversityYuanguo Bi - Northeastern University
- Resource Type
- Journal article
- Publication Details
- International journal of distributed sensor networks, Vol.14(6), p.155014771877968
- Publisher
- Sage
- DOI
- 10.1177/1550147718779680
- ISSN
- 1550-1477
- eISSN
- 1550-1477
- Number of pages
- 11
- Grant note
- 2017RALKFKT002 / State Key Laboratory of Rolling and Automation, Northeastern University 61501105 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) N171612014 / Fundamental Research Funds for the Central Universities
- Language
- English
- Date published
- 06/01/2018
- Academic Unit
- Computer Science
- Record Identifier
- 9984696577202771
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