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Sensors 2018, 18(9), 2886; https://doi.org/10.3390/s18092886

A Robust Terrain Aided Navigation Using the Rao-Blackwellized Particle Filter Trained by Long Short-Term Memory Networks

Department of Aerospace Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
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Author to whom correspondence should be addressed.
Received: 27 June 2018 / Revised: 16 August 2018 / Accepted: 27 August 2018 / Published: 31 August 2018
(This article belongs to the Section Physical Sensors)
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Abstract

Terrain-aided navigation (TAN) is a technology that estimates the position of the vehicle by comparing the altitude measured by an altimeter and height from the digital elevation model (DEM). The particle filter (PF)-based TAN has been commonly used to obtain stable real-time navigation solutions in cases where the unmanned aerial vehicle (UAV) operates at a high altitude. Even though TAN performs well on rough and unique terrains, its performance degrades in flat and repetitive terrains. In particular, in the case of PF-based TAN, there has been no verified technique for deciding its terrain validity. Therefore, this study designed a Rao-Blackwellized PF (RBPF)-based TAN, used long short-term memory (LSTM) networks to endure flat and repetitive terrains, and trained the noise covariances and measurement model of RBPF. LSTM is a modified recurrent neural network (RNN), which is an artificial neural network that recognizes patterns from time series data. Using this, this study tuned the noise covariances and measurement model of RBPF to minimize the navigation errors in various flight trajectories. This paper designed a TAN algorithm based on combining RBPF and LSTM and confirmed that it can enable a more precise navigation performance than conventional RBPF based TAN through simulations. View Full-Text
Keywords: terrain-aided navigation (TAN); Rao-Blackwellized particle filter (RBPF); long short-term memory (LSTM); terrain validity check; digital elevation model (DEM); inertial navigation system (INS) terrain-aided navigation (TAN); Rao-Blackwellized particle filter (RBPF); long short-term memory (LSTM); terrain validity check; digital elevation model (DEM); inertial navigation system (INS)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Lee, J.; Bang, H. A Robust Terrain Aided Navigation Using the Rao-Blackwellized Particle Filter Trained by Long Short-Term Memory Networks. Sensors 2018, 18, 2886.

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