@inproceedings{a09d4f47b2bd4376ae0372208d2ed041,
title = "A MLE-PSO indoor localization algorithm based on RSSI",
abstract = "Received signal strength indicator (RSSI) are mostly used to measure distance in wireless sensor networks (WSNs). It is difficult to avoid the error of RSSI ranging due to the complexity of the indoor environment. However, the localization error of the existing localization algorithm will increase greatly with the increase of ranging error. In order to improve the positioning accuracy, stability as well as the dynamic perfomance of localization, a MLE-PSO indoor localization algorithm based on RSSI is proposed in this paper. This new algorithm uses an optimization algorithm the traditional particle swarm optimization (PSO) for localization, and uses a traditional localization algorithm maximum likelihood estimation (MLE) to confine initial range and the area iterative process of PSO localization process. Simulation results show that the new algorithm improves the positioning accuracy and dynamic performance effectively compared with the PSO and MLE.",
keywords = "Indoor localization, Maximum Likelihood Estimation (MLE), Particle Swarm Optimization (PSO), RSSI Ranging Model, Received Signal Strength Indicator (RSSI), Wireless Sensor Network (WSN)",
author = "Chong Zhao and Bo Wang",
note = "Publisher Copyright: {\textcopyright} 2017 Technical Committee on Control Theory, CAA.; 36th Chinese Control Conference, CCC 2017 ; Conference date: 26-07-2017 Through 28-07-2017",
year = "2017",
month = sep,
day = "7",
doi = "10.23919/ChiCC.2017.8028312",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "6011--6015",
editor = "Tao Liu and Qianchuan Zhao",
booktitle = "Proceedings of the 36th Chinese Control Conference, CCC 2017",
address = "United States",
}