Terrain contour matching with recurrent neural networks

Lee, Seongheon; Bang, Hyochoong

doi:10.1109/aero.2018.8396591

Cited by 3 publications

(1 citation statement)

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“…For the robust TAN, the validity check technique of measurements by terrain roughness and uniqueness is an important technique that determines the navigation performance. The mean squared difference (MSD) and mean absolute difference (MAD) of the height deviation have been widely used for contour matching based TAN [ 14 , 15 ]. As for the bank of Kalman filter (BKF)-based TAN, the validity check technique that uses smoothed weighted residual squared (SWRS) is standard [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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

Lee

Bang

2018

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%