@inproceedings{68b1dad54cc743af9f9040b7d0e57108,
title = "Wireless sensor network localization based on PSO algorithm in NLOS environment",
abstract = "The localization accuracy of wireless sensor network(WSN) can be decreased due to the existence of the nonline of sight(NLOS) in real environment. This paper is focused on the NLOS node localization problem for WSN. Firstly, we use the modified Kalman Filter algorithm to reduce the NLOS error according to its distribution model. Moreover, combined with the least square method(LSM) method, the reconstructed measured value is used to estimate the general location of the target node. Finally, the higher localization accuracy can be obtained by applying the particle swarm optimization(PSO) algorithm. The experimental results show that the method can achieve a high localization accuracy in the complex NLOS environment.",
keywords = "Kalman Filter, LSM, Localization, NLOS, PSO, WSN",
author = "Yong Yang and Baokui Li and Bo Ye",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2016 ; Conference date: 11-09-2016 Through 12-09-2016",
year = "2016",
month = dec,
day = "13",
doi = "10.1109/IHMSC.2016.87",
language = "English",
series = "Proceedings - 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "292--295",
booktitle = "Proceedings - 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2016",
address = "United States",
}