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Open AccessArticle

A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing

1
School of automation, Nanjing University of Science and Technology, Nanjing 210094, China
2
Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute (FGI), Geodeetinrinne 2, FI-02431 Kirkkonummi, Finland
3
Key Laboratory of Quantitative Remote Sensing Information Technology, Chinese Academy of Sciences (CAS), Beijing 100094, China, [email protected] (W.L.)
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(2), 181; https://doi.org/10.3390/electronics8020181
Received: 8 December 2018 / Revised: 2 January 2019 / Accepted: 14 January 2019 / Published: 4 February 2019
(This article belongs to the Special Issue Selected Papers from IEEE ICKII 2018)
Currently, positioning, navigation, and timing information is becoming more and more vital for both civil and military applications. Integration of the global navigation satellite system and /inertial navigation system is the most popular solution for various carriers or vehicle positioning. As is well-known, the global navigation satellite system positioning accuracy will degrade in signal challenging environments. Under this condition, the integration system will fade to a standalone inertial navigation system outputting navigation solutions. However, without outer aiding, positioning errors of the inertial navigation system diverge quickly due to the noise contained in the raw data of the inertial measurement unit. In particular, the micromechanics system inertial measurement unit experiences more complex errors due to the manufacturing technology. To improve the navigation accuracy of inertial navigation systems, one effective approach is to model the raw signal noise and suppress it. Commonly, an inertial measurement unit is composed of three gyroscopes and three accelerometers, among them, the gyroscopes play an important role in the accuracy of the inertial navigation system’s navigation solutions. Motivated by this problem, in this paper, an advanced deep recurrent neural network was employed and evaluated in noise modeling of a micromechanics system gyroscope. Specifically, a deep long short term memory recurrent neural network and a deep gated recurrent unit–recurrent neural network were combined together to construct a two-layer recurrent neural network for noise modeling. In this method, the gyroscope data were treated as a time series, and a real dataset from a micromechanics system inertial measurement unit was employed in the experiments. The results showed that, compared to the two-layer long short term memory, the three-axis attitude errors of the mixed long short term memory–gated recurrent unit decreased by 7.8%, 20.0%, and 5.1%. When compared with the two-layer gated recurrent unit, the proposed method showed 15.9%, 14.3%, and 10.5% improvement. These results supported a positive conclusion on the performance of designed method, specifically, the mixed deep recurrent neural networks outperformed than the two-layer gated recurrent unit and the two-layer long short term memory recurrent neural networks. View Full-Text
Keywords: global navigation satellite system (GNSS); inertial navigation system (INS); long short term memory (LSTM); gated recurrent unit (GRU); microelectronics system (MEMS) global navigation satellite system (GNSS); inertial navigation system (INS); long short term memory (LSTM); gated recurrent unit (GRU); microelectronics system (MEMS)
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MDPI and ACS Style

Jiang, C.; Chen, Y.; Chen, S.; Bo, Y.; Li, W.; Tian, W.; Guo, J. A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing. Electronics 2019, 8, 181. https://doi.org/10.3390/electronics8020181

AMA Style

Jiang C, Chen Y, Chen S, Bo Y, Li W, Tian W, Guo J. A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing. Electronics. 2019; 8(2):181. https://doi.org/10.3390/electronics8020181

Chicago/Turabian Style

Jiang, Changhui; Chen, Yuwei; Chen, Shuai; Bo, Yuming; Li, Wei; Tian, Wenxin; Guo, Jun. 2019. "A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing" Electronics 8, no. 2: 181. https://doi.org/10.3390/electronics8020181

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