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Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning Machine
Joint Authors
Lee, Jungshin
Sung, Changky
Oh, Juhyun
Source
International Journal of Aerospace Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-11
Country of Publication
Egypt
No. of Pages
11
Abstract EN
A high-resolution digital elevation model (DEM) is an important element that determines the performance of terrain referenced navigation (TRN).
However, the higher the resolution of the DEM, the bigger the memory size needed for storing it.
It is difficult to secure such large memory spaces in small, low-priced unmanned aerial vehicles.
In this study, a high-precision terrain regression model to fit the DEM is generated using the extreme learning machine technique based on the multilayer radial basis function.
The TRN results using the proposed method are compared with existing studies on various DEM fitting methods.
This study verifies that the proposed method obtains improved fitting accuracy and TRN performance over existing DEM fitting methods such as bilinear interpolation, SVM for regression, and bi-spline neural network, without the DEM storage space.
American Psychological Association (APA)
Lee, Jungshin& Sung, Changky& Oh, Juhyun. 2019. Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning Machine. International Journal of Aerospace Engineering،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1157124
Modern Language Association (MLA)
Lee, Jungshin…[et al.]. Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning Machine. International Journal of Aerospace Engineering No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1157124
American Medical Association (AMA)
Lee, Jungshin& Sung, Changky& Oh, Juhyun. Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning Machine. International Journal of Aerospace Engineering. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1157124
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1157124