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

Abstract

Inertial measurement unit (IMU) (an IMU usually contains three gyroscopes and accelerometers) is the key sensor to construct a selfcontained 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. e 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.

A Combined Method for MEMS Gyroscope Error Compensation Using a Long Short-Term Memory Network and Kalman Filter in Random Vibration Environments

“…The long short-term memory network is a variant of the recurrent neural network used to solve the gradient vanishing or gradient explosion problem of RNNs [ 30 , 31 ]. A detailed description of LSTM units can be found in references [ 15 , 16 , 17 , 18 ].…”

“…Scholars have proposed methods such as the autoregressive moving average (ARMA) model, the Allan variance (AV), the wavelet threshold (WT), the support vector machine (SVM), and the artificial neural network (ANN), and all of them have achieved excellent results [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. Recently, various variants of the recurrent neural network (RNN), which has strong processing power for time-series data, have been shown to be superior to traditional methods in the research of error compensation in MEMS gyroscopes [ 15 , 16 , 17 , 18 ].…”

Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

“…Different from the conventional method, NAS-RNN was able to search a more feasible architecture for the selected application. Recently, this method was employed in MEMS gyroscope noise suppressing, and achieves considerable improvement for the MEMS gyroscope output signal [ 71 ]. The basic architecture of NAS-RNN is presented as follows in Figure 17 .…”

FBLS-Based Fusion Method for Unmanned Surface Vessel Positioning Considering Denoising Algorithm

“…At present, the multi-resolution ability of wavelet transform denoising techniques is utilized to eliminate the short-term errors in IMU, and the results show that the noise is suppressed by reducing the frequency components with small correlation [8,9]. Unfortunately, the longterm errors of sensor associated with ship motion dynamics cannot be thoroughly removed by using the traditional denoising approached based on wavelet transform [10]. There are some common theories that can be utilized to pretreat the dynamic data from the original IMU measurement, for instance the Allan variance-based method [11,12].…”