IMU Data and GPS Position Information Direct Fusion Based on LSTM
1
College of Intelligent System Science and Engineering, Harbin Engineering University, Harbin 150001, China
2
Beijing Institute of Control and Electronic Technology, Beijing 100032, China
*
Author to whom correspondence should be addressed.
Academic Editors: Antonio Moschitta and Javier Bajo
Sensors 2021, 21(7), 2500; https://doi.org/10.3390/s21072500
Received: 12 January 2021
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Revised: 30 March 2021
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Accepted: 31 March 2021
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Published: 3 April 2021
(This article belongs to the Section Intelligent Sensors)
In recent years, the application of deep learning to the inertial navigation field has brought new vitality to inertial navigation technology. In this study, we propose a method using long short-term memory (LSTM) to estimate position information based on inertial measurement unit (IMU) data and Global Positioning System (GPS) position information. Simulations and experiments show the practicability of the proposed method in both static and dynamic cases. In static cases, vehicle stop data are simulated or recorded. In dynamic cases, uniform rectilinear motion data are simulated or recorded. The value range of LSTM hyperparameters is explored through both static and dynamic simulations. The simulations and experiments results are compared with the strapdown inertial navigation system (SINS)/GPS integrated navigation system based on kalman filter (KF). In a simulation, the LSTM method’s computed position error Standard Deviation (STD) was 52.38% of what the SINS computed. The biggest simulation radial error estimated by the LSTM method was 0.57 m. In experiments, the LSTM method computed a position error STD of 23.08% using only SINSs. The biggest experimental radial error the LSTM method estimated was 1.31 m. The position estimated by the LSTM fusion method has no cumulative divergence error compared to SINS (computed). All in all, the trained LSTM is a dependable fusion method for combining IMU data and GPS position information to estimate position.
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MDPI and ACS Style
Guang, X.; Gao, Y.; Liu, P.; Li, G. IMU Data and GPS Position Information Direct Fusion Based on LSTM. Sensors 2021, 21, 2500. https://doi.org/10.3390/s21072500
AMA Style
Guang X, Gao Y, Liu P, Li G. IMU Data and GPS Position Information Direct Fusion Based on LSTM. Sensors. 2021; 21(7):2500. https://doi.org/10.3390/s21072500
Chicago/Turabian StyleGuang, Xingxing, Yanbin Gao, Pan Liu, and Guangchun Li. 2021. "IMU Data and GPS Position Information Direct Fusion Based on LSTM" Sensors 21, no. 7: 2500. https://doi.org/10.3390/s21072500
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