Publication Type

Journal Article

Version

publishedVersion

Publication Date

2-2012

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.

Keywords

Unmanned aerial vehicles (UAVs), Extreme learning machines (ELM), Terrain-based navigation

Discipline

Computer and Systems Architecture | Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Neural Computing and Applications

Volume

22

First Page

469

Last Page

477

ISSN

0941-0643

Identifier

10.1007/s00521-012-0866-9

Publisher

Springer (part of Springer Nature): Springer Open Choice Hybrid Journals

Additional URL

https://doi.org/10.1007/s00521-012-0866-9

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