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Open AccessArticle

A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages

1
GNSS Research Center, Wuhan University, Wuhan 430079, China
2
National Engineering Research Center for Satellite Positioning System, Wuhan 430019, China
3
School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
4
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 256; https://doi.org/10.3390/rs12020256
Received: 10 December 2019 / Revised: 5 January 2020 / Accepted: 7 January 2020 / Published: 10 January 2020
Aiming to improve the navigation accuracy during global navigation satellite system (GNSS) outages, an algorithm based on long short-term memory (LSTM) is proposed for aiding inertial navigation system (INS). The LSTM algorithm is investigated to generate the pseudo GNSS position increment substituting the GNSS signal. Almost all existing INS aiding algorithms, like the multilayer perceptron neural network (MLP), are based on modeling INS errors and INS outputs ignoring the dependence of the past vehicle dynamic information resulting in poor navigation accuracy. Whereas LSTM is a kind of dynamic neural network constructing a relationship among the present and past information. Therefore, the LSTM algorithm is adopted to attain a more stable and reliable navigation solution during a period of GNSS outages. A set of actual vehicle data was used to verify the navigation accuracy of the proposed algorithm. During 180 s GNSS outages, the test results represent that the LSTM algorithm can enhance the navigation accuracy 95% compared with pure INS algorithm, and 50% of the MLP algorithm. View Full-Text
Keywords: LSTM; INS/GNSS integrated navigation system; GNSS outage; pseudo measurement estimating LSTM; INS/GNSS integrated navigation system; GNSS outage; pseudo measurement estimating
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MDPI and ACS Style

Fang, W.; Jiang, J.; Lu, S.; Gong, Y.; Tao, Y.; Tang, Y.; Yan, P.; Luo, H.; Liu, J. A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages. Remote Sens. 2020, 12, 256.

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