A low-cost indoor localization system based on received signal strength indicator by modifying trilateration for harsh environments. (June 2018)

• Record Type: Journal Article
• Authors: Tong, Haibin; Deng, Qingxu; Zhang, Tianyu; Bi, Yuanguo
• Is Part Of: International journal of distributed sensor networks. Volume 14:Number 6(2018)
• Publication Date: 2018-06
• Subjects: Indoor localization -- Internet of things -- curve fitting -- Kalman filter -- trilateration; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147718779680
• Languages: English
• ISSNs: 1550-1329

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.