A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing

Changhui Jiang,Yuming Bo,Shuai Chen,Wenxin Tian,Yuwei Chen,Jun Guo,Wei Li

doi:10.3390/electronics8020181

Abstract

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.

Highlights

With the booming of location based services (LBS), positioning, navigation, and timing (PNT)information is more essential than at any time in human history, since more and more smart devicesElectronics 2019, 8, 181; doi:10.3390/electronics8020181 www.mdpi.com/journal/electronicsElectronics 2019, 8, 181 relies on PNT information [1]
The MEMS inertial measurement unit (IMU) is same model that is employed in our the details are listed in Table 1 [33]
49.3%, and 64.4%, 53.3% improvements in attitude errors, thein attitude errors, the two-layer Gated Recurrent Unit (GRU) performed 56.3%, 54.5%, and 47.9% decreases in two-layer GRU performed 56.3%, 54.5%, and 47.9% decreases in attitude errors, and the attitude attitude errors, and the attitude errors of Long Short Term Memory (LSTM)–GRU decreased by 72.2%, 69.3%, and errors of LSTM–GRU decreased by 72.2%, 69.3%, and 58.4%

Summary

Introduction

With the booming of location based services (LBS), positioning, navigation, and timing (PNT)information is more essential than at any time in human history, since more and more smart devicesElectronics 2019, 8, 181; doi:10.3390/electronics8020181 www.mdpi.com/journal/electronicsElectronics 2019, 8, 181 relies on PNT information [1]. The advantages of GNSS are summarized as: (1) GNSS is able to provide precise navigation solutions at low cost, since a handheld chip receiver is cheap and sufficient for common applications; (2) GNSS is an all-weather navigation system covering the earth, and its positioning accuracy does not diverge over time Apart from these advantages, it has some drawbacks limiting its further application: (1) firstly, the satellites are far away from the Earth, the signals are pretty weak when they reach the Earth; (2) secondly, GNSS civil signal structure is open to the public, which makes

Methods

Results

Discussion

Conclusion