Vision-based terrain referenced navigation of aerial vehicles using an adaptive extended Kalman filter

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 130
  • Download : 0
This paper addresses a vision-based terrain referenced navigation of an aircraft. A digital terrain map, in the surroundings of the aircraft, is compared with the camera measurements to estimate the aircraft position. Generally, the measurement equation in the terrain referenced navigation is highly nonlinear due to the sharp changes of terrain. Thus, the conventional extended Kalman filter could lead to unstable navigation solutions. In this paper, a new approach using an adaptive extended Kalman filter is proposed to cope up with the nonlinearity problem. A least squares method is utilized to derive the linearized measurement equations. The Jacobian matrix and sensor noise covariance are modified as a means of smoothing the sharp changes of terrain. Monte Carlo simulations verify that the proposed filter gives the stable navigation solutions, even when there is a large initial error, which is the primary reason for the filter divergence. Moreover, the proposed adaptation barely requires additional computational burden, whereas the high-order filters such as particle filter generally needs higher computational power.
Publisher
SAGE PUBLICATIONS LTD
Issue Date
2018-06
Language
English
Article Type
Article
Keywords

AIDED INERTIAL NAVIGATION; POSITION ESTIMATION; MOTION ESTIMATION; UAV NAVIGATION; SYSTEM; INFORMATION; VELOCITY

Citation

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING, v.232, no.8, pp.1584 - 1597

ISSN
0954-4100
DOI
10.1177/0954410017699431
URI
http://hdl.handle.net/10203/242618
Appears in Collection
AE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0