Terrain Contour Matching with Recurrent Neural Networks

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In this paper, Long Short-Term Memory, a special kind of recurrent neural network, is tested to search the feasibility of using machine learning approaches on the Terrain Referenced Navigation. By using the machine learning approach, we tried not to switch but to merge each strength of existing TRN algorithms; known as sequential, and batch processing methods; and make up their exclusive weaknesses. From our experiments, RNN structure originally designed to process natural languages, handwritten characters, and image captioning, also showed the possibility to learn how to read a terrain elevation map data to distinguish the current location of an aircraft. In this regard, we are trying to modify our network design to enhance the position estimation accuracy, and implement the system into real-world with the larger scale worldwide map data.
Publisher
IEEE
Issue Date
2018-03-04
Language
English
Citation

IEEE Aerospace Conference 2018

DOI
10.1109/AERO.2018.8396591
URI
http://hdl.handle.net/10203/273563
Appears in Collection
AE-Conference Papers(학술회의논문)
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