Title: Lightning location method based on improved fuzzy C-means clustering algorithm

Authors: Tao Li; Jie Chen; Lina Wang; Yongjun Ren

Addresses: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Jiangsu, Nanjing, 210-044, China ' School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Jiangsu, Nanjing, 210-044, China ' School of Artificial Intelligence, Nanjing University of Information Science and Technology, Jiangsu, Nanjing, 210-044, China ' School of Computer Software, Nanjing University of Information Science and Technology, Jiangsu, Nanjing, 210-044, China

Abstract: Location accuracy is an important index for the evaluation of location networks and the localisation algorithm related to the accuracy of the results. Classical location algorithms scarcely correct errors accurately. Moreover, they have poor resistance to error interference and low location accuracy. To achieve error-resistant lightning localisation, weighted rough-fuzzy C-means (WRFCM) was introduced in location calculation. The performance of this localisation algorithm was analysed using a lightning accident case through regional simulation. Results show that the lightning localisation algorithm based on WFCM overcomes the disadvantage in which traditional location algorithms easily diverge; the algorithm also has an improved ability to resist error interference and can solve the lightning points steadily and accurately.

Keywords: lightning localisation; TDOA; time difference of arrival; cluster analysis; FCM; fuzzy C-means; weighted rough-fuzzy C-means; regional simulation; location accuracy; location calculation.

DOI: 10.1504/IJSNET.2021.113833

International Journal of Sensor Networks, 2021 Vol.35 No.3, pp.133 - 142

Received: 09 Mar 2020
Accepted: 20 Jun 2020
Published online: 31 Mar 2021 *

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