Performance Analysis of a Deep Simple Recurrent Unit Recurrent Neural Network (SRU-RNN) in MEMS Gyroscope De-Noising

Abstract

Microelectromechanical System (MEMS) Inertial Measurement Unit (IMU) is popular in the community for constructing a navigation system, due to its small size and low power consumption. However, limited by the manufacturing technology, MEMS IMU experiences more complicated noises and errors. Thus, noise modeling and suppression is important for improving accuracy of the navigation system based on MEMS IMU. Motivated by this problem, in this paper, a deep learning method was introduced to MEMS gyroscope de-noising. Specifically, a recently popular Recurrent Neural Networks (RNN) variant Simple Recurrent Unit (SRU-RNN) was employed in MEMS gyroscope raw signals de-noising. A MEMS IMU MSI3200 from MT Microsystem Company was employed in the experiments for evaluating the proposed method. Following two problems were furtherly discussed and investigated: (1) the employed SRU with different training data length were compared to explore whether there was trade-off between the training data length and prediction performance; (2) Allan Variance was the most popular MEMS gyroscope analyzing method, and five basic parameters were employed to describe the performance of different grade MEMS gyroscope; among them, quantization noise, angle random walk, and bias instability were the major factors influencing the MEMS gyroscope accuracy, the compensation results of the three parameters for gyroscope were presented and compared. The results supported the following conclusions: (1) considering the computation brought from training dataset, the values of 500, 3000, and 3000 were individually sufficient for the three-axis gyroscopes to obtain a reliable and stable prediction performance; (2) among the parameters, the quantization noise, angle random walk, and bias instability performed 0.6%, 6.8%, and 12.5% improvement for X-axis gyroscope, 60.5%, 17.3%, and 34.1% improvement for Y-axis gyroscope, 11.3%, 22.7%, and 35.7% improvement for Z-axis gyroscope, and the corresponding attitude errors decreased by 19.2%, 82.1%, and 69.4%. The results surely demonstrated the effectiveness of the employed SRU in this application.

A New Efficient Filtering Model for GPS/SINS Ultratight Integration System

“…Smartphones, vehicles, pedestrians, and ships all employ GPS to provide continuous and precise positioning, navigation, and timing (PNT) information [1][2][3][4][5]. With the satellites in space covering the earth, the GPS receiver around the world can receive the broadcast satellite signals and output PNT information [1][2][3][4][5]. With the fast development of GPS receiver design and manufacturing technology, currently, a low-cost handheld receiver is capable of positioning accurately in open sky.…”

“…However, for a standalone GPS receiver, signal challenging environment might hinder its extensive applications, for instance, NLOS (none-ofsight) and multipath (MP) signal in urban canyons and dynamic stress under high dynamic; these negative factors may affect the signal availability or navigation solution accuracy and integrity. Without enough satellites with "clean" signals available, the receiver will fail to output correct PNT information [2][3][4][5][6][7][8]. For instance, the NLOS signals will cause the pseudorange bias, signal blockage will influence the satellite geometry distribution, and the MP will also influence the errors in pseudorange measurements [2][3][4][5][6][7][8][9][10][11][12][13][14].…”

A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing

“…Recently, Deep Learning (DL) has gained a boom and performed excellently in various applications including image processing, Nature Language Processing (NLP) and sequential signal processing [30][31][32][33][34][35][36][37]. In aspects of time series processing, a recurrent neural network (RNN) was always the most feasible selection [30][31][32][33][34][35][36][37]. A common RNN was not sufficient, thus, variants of RNN were proposed for enhancing the performance.…”

“…Among the variants, Long Short Term Memory (LSTM) and the Gated Recurrent Unit (GRU) were most popular. LSTM-RNN and GRU-RNN both obtained excellent performance in NLP [30][31][32][33][34][35][36][37]. In addition, in our previous paper, LSTM was employed and compared in MEMS gyroscope de-noising [8].…”

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

“…Specially, Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) and Simple Reduced Unit Recurrent Neural Network (SRU-RNN) were employed in MEMS gyroscope noise suppressing. Both LSTM-RNN and SRU-RNN were popular variants of RNN, and they both performed better than conventional machine learning or regression method [32,33]. However, it is interesting to explore a more feasible RNN structure in this application.…”