期刊
IEEE ACCESS
卷 8, 期 -, 页码 152512-152522出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3016004
关键词
Global navigation satellite system; Micromechanical devices; Land vehicles; Sensors; Biological neural networks; Artificial intelligence; global navigation satellite system; inertial navigation; sensor fusion; parameter estimation
资金
- National Natural Science Foundation of China [41704029]
- Sichuan Province Science and Technology Project [2018CC0018, 2018GZDZX0013, 2018SZ0364]
How to limit the drifts of the navigation errors in an inertial navigation system (INS) with low-cost sensors is one of the main challenges for the land vehicle navigations. In this paper, we present a novel hybrid navigation strategy to integrate the Micro-Electric-Mechanic-systems (MEMS) INS, odometer (OD) and global navigation satellite systems (GNSS), with aim to enhance the positioning accuracy of the inertial system during GNSS outages. To accurately estimate the INS error states, the neural network (NN) is proposed to mimic the velocity of the navigation frame with the data from the MEMS INS, odometer, as well as the non-holonomic constraints (NHC). The long short-term memory (LSTM) NN is adopted in our approach due to its ability to adaptively use the data in the past. The road tests are conducted with two different MEMS IMUs to verify the proposed navigation strategy. Comparing to the traditional integrated MEMS INS/OD/GNSS system based on the extended Kalman filtering (EKF), our hybrid approach provides over 60% improvements in terms of the root mean square (RMS) and maximum horizontal position errors during GNSS outages.
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