A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing

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.

A MEMS Gyroscope Noise Suppressing Method Using Neural Architecture Search Neural Network

“…Actually, the NAS-RNN was more complicated than LSTM-RNN, and NAS-RNN had a more heavy computation Step Figure 7: Comparison of X-axis gyroscope denoising results. 6 Mathematical Problems in Engineering load. More specifications were introduced in Section 2.2.…”

“…Global Navigation Satellite System (GNSS) receiver has been the indispensable equipment for various vehicles, carriers, and smart devices, for instance, unmanned ground vehicles (UGV), unmanned aerial vehicle (UAV), smartphone, and so on [1][2][3][4][5]. With a GNSS receiver, these users are able to obtain accurate PVT information under an open-sky environment [6,7].…”

Accelerometer-Based Gyroscope Drift Compensation Approach in a Dual-Axial Stabilization Platform

“…The core of MEMS inertial sensor is the inertial measurement unit (IMU), which consists of a tri-axis gyro and tri-axis accelerometer to measure the angular rate and the linear acceleration of the carrier. However, gyro drift error from integral calculation in long time is not to be neglected [4,5]. Hence, a proper compensation method for gyro drift is necessary to improve the accuracy and stability of the gyro stabilization platform.…”

Evaluation of Mixed Deep Neural Networks for Reverberant Speech Enhancement

“…Such type of deep neural network algorithms have been successful in overcoming the performance of classical methods based on signal processing, which have considered various signal-to-noise (SNR) [12][13][14][15], or reverberant speech [16][17][18]. Some recent work has explored the use of Mixed Neural Networks to achieve a better performance in different tasks, such as classifying the temporary stages of sleep, analyzing the real-time behavior of an online buyer or the suppression of noise in a MEMS gyroscope, in which good results were obtained for specific situations and configurations [19], [20], [21].…”