@inproceedings{a34ca835a21a4a21bd01a805bcea102e,
title = "Unscented Kalman filter for position estimation of UAV by using image information",
abstract = "In this paper, the position estimation problem is solved by using unscented Kalman filter with observation uncertainty's compensations. These observation uncertainties causing the estimation become inaccurate in position estimate problem. Visual observation is difficult for an unmanned aerial vehicle equipped with only a monocular-vision camera and often results in observation error because of the detected blurred images. A method to weight the observations is proposed in order to improve the position estimation. Simulation is performed using MATLAB and Simulink to verify the proposed method. The simulation result shows that the proposed method can estimate the position accurately.",
keywords = "EKF, Position Estimation, UAV, UKF",
author = "Tang, {Swee Ho} and Takaaki Kojima and Toru Namerikawa and Yeong, {Che Fai} and Su, {Eileen Lee Ming}",
note = "Publisher Copyright: {\textcopyright} 2015 The Society of Instrument and Control Engineers-SICE.; 54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015 ; Conference date: 28-07-2015 Through 30-07-2015",
year = "2015",
month = sep,
day = "30",
doi = "10.1109/SICE.2015.7285427",
language = "English",
series = "2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "695--700",
booktitle = "2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015",
}