@article{bf129fe099014749a47a6a2d5f470dbd,
title = "An RSSI classification and tracing algorithm to improve trilateration-based positioning",
abstract = "Received signal strength indicator (RSSI)-based positioning is suitable for large-scale applications due to its advantages of low cost and high accuracy. However, it suffers from low stability because RSSI is easily blocked and easily interfered with by objects and environmental effects. Therefore, this paper proposed a tri-partition RSSI classification and its tracing algorithm as an RSSI filter. The proposed filter shows an available feature, where small test RSSI samples gain a low deviation of less than 1 dBm from a large RSSI sample collected about 10 min, and the sub-classification RSSIs conform to normal distribution when the minimum sample count is greater than 20. The proposed filter also offers several advantages compared to the mean filter, including lower variance range with an overall range of around 1 dBm, 25.9% decreased sample variance, and 65% probability of mitigating RSSI left-skewness. We experimentally confirmed the proposed filter worked in the path-loss exponent fitting and location computing, and a 4.45-fold improvement in positioning stability based on the sample standard variance, and positioning accuracy improved by 20.5% with an overall error of less than 1.46 m.",
keywords = "Accuracy, RSSI classification, RSSI filter, Stability, Trilateral indoor positioning",
author = "Yong Shi and Shi, {Wen Zhong} and Xintao Liu and Xianjian Xiao",
note = "Funding Information: Funding: This work was supported in part by the National Natural Science Foundation of China (Grant No. 41571382, No. 41901323), in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 15KJB170006, No.16KJB 520003, No. 17KJD520001), in part by Jiangsu industry-university-institute collaboration project (Grant No. BY2019021), in part by Jiangsu Collaborative Innovation Center for Cultural Creativity (Grant No.XYN1702, No.XYN1703), in part by Taizhou Science and technology support program of China (Grant No. TS201621), in part by Changzhou Science and technology support program of China (Grant No. CE20172023), in part by Collaborative Innovation Center of Changzhou Institute of Technology for Digital Information Technology, in part by Excellent Scientific and Technological Innovation Team of Changzhou Institute of Technology, and in part by Natural Science Foundation of Changzhou Institute of Technology (Grant No.YN1726). Funding Information: This work was supported in part by the National Natural Science Foundation of China (Grant No. 41571382, No. 41901323), in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 15KJB170006, No.16KJB 520003, No. 17KJD520001), in part by Jiangsu industry-university-institute collaboration project (Grant No. BY2019021), in part by Jiangsu Collaborative Innovation Center for Cultural Creativity (Grant No.XYN1702, No.XYN1703), in part by Taizhou Science and technology support program of China (Grant No. TS201621), in part by Changzhou Science and technology support program of China (Grant No. CE20172023), in part by Collaborative Innovation Center of Changzhou Institute of Technology for Digital Information Technology, in part by Excellent Scientific and Technological Innovation Team of Changzhou Institute of Technology, and in part by Natural Science Foundation of Changzhou Institute of Technology (Grant No.YN1726). Publisher Copyright: {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2020",
month = aug,
day = "1",
doi = "10.3390/s20154244",
language = "English",
volume = "20",
pages = "1--17",
journal = "Sensors (Switzerland)",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "15",
}