Please use this identifier to cite or link to this item:
|Title:||Extreme learning machine terrain-based navigation for unmanned aerial vehicles|
|Keywords:||Extreme learning machines (ELM)|
Unmanned aerial vehicles (UAVs)
|Source:||Kan, E.M., Lim, M.H., Ong, Y.S., Tan, A.H., Yeo, S.P. (2013). Extreme learning machine terrain-based navigation for unmanned aerial vehicles. Neural Computing and Applications 22 (3-4) : 469-477. ScholarBank@NUS Repository. https://doi.org/10.1007/s00521-012-0866-9|
|Abstract:||Unmanned aerial vehicles (UAVs) rely on global positioning system (GPS) information to ascertain its position for navigation during mission execution. In the absence of GPS information, the capability of a UAV to carry out its intended mission is hindered. In this paper, we learn alternative means for UAVs to derive real-time positional reference information so as to ensure the continuity of the mission. We present extreme learning machine as a mechanism for learning the stored digital elevation information so as to aid UAVs to navigate through terrain without the need for GPS. The proposed algorithm accommodates the need of the on-line implementation by supporting multi-resolution terrain access, thus capable of generating an immediate path with high accuracy within the allowable time scale. Numerical tests have demonstrated the potential benefits of the approach. © 2012 Springer-Verlag London Limited.|
|Source Title:||Neural Computing and Applications|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 15, 2018
WEB OF SCIENCETM
checked on Feb 7, 2018
checked on Feb 13, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.