Title :
Unscented Kalman filter for position estimation of UAV by using image information
Author :
Swee Ho Tang;Takaaki Kojima;Toru Namerikawa;Che Fai Yeong;Eileen Lee Ming Su
Author_Institution :
Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
fDate :
7/1/2015 12:00:00 AM
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 :
"Kalman filters","Aircraft","Mathematical model","Estimation","Atmospheric modeling","Cameras","Electronic mail"
Conference_Titel :
Society of Instrument and Control Engineers of Japan (SICE), 2015 54th Annual Conference of the
DOI :
10.1109/SICE.2015.7285427