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

Zhenshu Zhu,Yuming Bo,Changhui Jiang

doi:10.1155/2019/5491243

Abstract

Inertial measurement unit (IMU) (an IMU usually contains three gyroscopes and accelerometers) is the key sensor to construct a self-contained inertial navigation system (INS). IMU manufactured through the Micromechanics Electronics Manufacturing System (MEMS) technology becomes more popular, due to its smaller column, lower cost, and gradually improved accuracy. However, limited by the manufacturing technology, the MEMS IMU raw measurement signals experience complicated noises, which cause the INS navigation solution errors diverge dramatically over time. For addressing this problem, an advanced Neural Architecture Search Recurrent Neural Network (NAS-RNN) was employed in the MEMS gyroscope noise suppressing. NAS-RNN was the recently invented artificial intelligence method for time series problems in data science community. Different from conventional method, NAS-RNN was able to search a more feasible architecture for selected application. In this paper, a popular MEMS IMU STIM300 was employed in the testing experiment, and the sampling frequency was 125 Hz. The experiment results showed that the NAS-RNN was effective for MEMS gyroscope denoising; the standard deviation values of denoised three-axis gyroscope measurements decreased by 44.0%, 34.1%, and 39.3%, respectively. Compared with the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), the NAS-RNN obtained further decreases by 28.6%, 3.7%, and 8.8% in standard deviation (STD) values of the signals. In addition, the attitude errors decreased by 26.5%, 20.8%, and 16.4% while substituting the LSTM-RNN with the NAS-RNN.

Highlights

With the booming of the location-based service (LBS), the demand for position, velocity, and time (PVT) information has gained a significant increment [1,2,3,4,5]
A popular Micromechanics Electronics Manufacturing System (MEMS) Inertial measurement unit (IMU) STIM300 was adopted, which was manufactured by Sensors AS Company from Norway. e IMU was presented in Figure 4, the gyroscope’s full measurement range was ± 400/s, the gyroscope’s bi√as instability was 0.3°/h, the angle random walk was ≤0.15°/ hr, and the sampling frequency was 125 Hz [37]
This section is organized as follows: (1) First subpart presented the setting up of the NASRNN, and the results of the Neural Architecture Search Recurrent Neural Network (NAS-RNN), the training loss, the standard deviation values, and attitude errors were employed as indicators for evaluating the performance of the NAS-RNN denoising

Summary

Introduction

With the booming of the location-based service (LBS), the demand for position, velocity, and time (PVT) information has gained a significant increment [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]. For GNSS, the navigation satellites in orbit emit the signals to the earth, and the receivers get the signal for obtaining distance between the user and satellite through measuring the transmitting time of the navigation signal. The following two reasons account for this phenomena: (1) the transmitting signal power is limited by the energy of the navigation satellite, saving the energy and for keeping the satellite life span, it is hard to enlarge the navigation signal strength will consume more energy; (2) for saving the cost of constructing the GNSS, while meeting the demand of covering the earth with fewer satellites, the orbit is far away from the earth and the signal transmits a long distance before reaching the earth [8,9,10,11]. Due to the above drawbacks, a standalone GNSS is usually unable to output seamless and ubiquitous navigation solutions. us, it is of significance for enhancing the performance of the GNSS in signal challenging environments

Methods

Results

Conclusion