@inproceedings{0de7cbedf1ee4bdabd917c950e720c80,
title = "A Novel Indoor Intelligent Location Algorithm Based on GA-BFO",
abstract = "The error caused by nonline-of-sight (NLOS) is main factor affecting the indoor wireless positioning accuracy. In order to eliminate the NLOS error and improve the positioning accuracy, genetic algorithm, genetic algorithm-Hill Climbing algorithm and genetic algorithm-Bacteria Foraging Optimization algorithm are applied to time difference of arrival (TDOA) positioning optimization in this paper. Research results show genetic algorithm-Bacteria Foraging Optimization algorithm, combined global search with local search, has the best performance in terms of positioning accuracy and convergence speed. This method is better in eliminating the NLOS error and improving the performance of real-time positioning.",
keywords = "Convergence speed, Indoor location, NLOS error, Positioning accuracy, RMSE, TDOA",
author = "Zhang Lan and Ma Hongmei and Sun Changyin and Wu Xinqiao",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 3rd International Conference on Information Science and Control Engineering, ICISCE 2016 ; Conference date: 08-07-2016 Through 10-07-2016",
year = "2016",
month = oct,
day = "31",
doi = "10.1109/ICISCE.2016.261",
language = "英语",
series = "Proceedings - 2016 3rd International Conference on Information Science and Control Engineering, ICISCE 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1222--1225",
editor = "Shaozi Li and Yun Cheng and Ying Dai",
booktitle = "Proceedings - 2016 3rd International Conference on Information Science and Control Engineering, ICISCE 2016",
}