A Novel Tri-Axial MEMS Gyroscope Calibration Method over a Full Temperature Range

Single-axis rotational inertial navigation systems (single-axis RINSs) are widely used in high-accuracy navigation because of their ability to restrain the horizontal axis errors of the inertial measurement unit (IMU). The IMU errors, especially the biases, should be constant during each rotation cycle that is to be modulated and restrained. However, the temperature field, consisting of the environment temperature and the power heating of single-axis RINS, affects the IMU performance and changes the biases over time. To improve the precision of single-axis RINS, the change of IMU biases caused by the temperature should be calibrated accurately. The traditional thermal calibration model consists of the temperature and temperature change rate, which does not reflect the complex temperature field of single-axis RINS. This paper proposed a multiple regression method with a temperature gradient in the model, and in order to describe the complex temperature field thoroughly, a BP neural network method is proposed with consideration of the coupled items of the temperature variables. Experiments show that the proposed methods outperform the traditional calibration method. The navigation accuracy of single-axis RINS can be improved by up to 47.41% in lab conditions and 65.11% in the moving vehicle experiment, respectively.

Section: Of 18mentioning

confidence: 99%

Thermal Modeling and Calibration Method in Complex Temperature Field for Single-Axis Rotational Inertial Navigation System

Wang

Cheng

Du³

2020

“…Because micro-electromechanical system (MEMS) inertial sensors have obvious advantages in weight, cost, and power consumption, MEMS inertial sensors are widely used in UAVs to perform inertial measuring tasks. However, the performance of MEMS inertial sensors can be significantly affected by external temperature [ 4 , 5 , 6 ] so diagnosing this type of fault is crucial in guaranteeing the reliable control of UAVs.…”

Section: Introductionmentioning

confidence: 99%

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert–Huang Transform and Deep Learning

Gao

Wei

Zhou

et al. 2020

In this paper, we propose a novel method for fault diagnosis in micro-electromechanical system (MEMS) inertial sensors using a bidirectional long short-term memory (BLSTM)-based Hilbert–Huang transform (HHT) and a convolutional neural network (CNN). First, the method for fault diagnosis of inertial sensors is formulated into an HHT-based deep learning problem. Second, we present a new BLSTM-based empirical mode decomposition (EMD) method for converting one-dimensional inertial data into two-dimensional Hilbert spectra. Finally, a CNN is used to perform fault classification tasks that use time–frequency HHT spectrums as input. According to our experimental results, significantly improved performance can be achieved, on average, for the proposed BLSTM-based EMD algorithm in terms of EMD computational efficiency compared with state-of-the-art algorithms. In addition, the proposed fault diagnosis method achieves high accuracy in fault classification.

“…To compensate for the inertial errors caused by the fluctuation in the environment temperature [ 12 ], two kinds of methods have been proposed as follows. One is the passive method [ 10 , 13 ], which can include thermal compensation, thermal shunting of elements, and special structural design. Yang et al [ 13 ] used tri-axial Micro-Electro-Mechanical System (MEMS) gyroscope outputs in different temperatures to calculate the error parameters of different temperature points for error compensation of gyroscope caused by temperature fluctuation.…”

Section: Introductionmentioning

confidence: 99%

“…One is the passive method [ 10 , 13 ], which can include thermal compensation, thermal shunting of elements, and special structural design. Yang et al [ 13 ] used tri-axial Micro-Electro-Mechanical System (MEMS) gyroscope outputs in different temperatures to calculate the error parameters of different temperature points for error compensation of gyroscope caused by temperature fluctuation. The other is the active method, which involves designing a temperature control system [ 14 , 15 , 16 , 17 , 18 , 19 , 20 ] to keep the temperature of the inertial sensors within a certain range while maintaining a high accuracy value.…”

Section: Introductionmentioning

confidence: 99%

“…However, the effect of the heading rotation is often neglected in the temperature tests. Temperature chambers to support the heading rotation and temperature change for the gyroscopes of low-precision, small-sized, and lightweight MEMS INSs are available [ 13 ]. Nevertheless, no evaluation system can support the high–low temperature and heading rotation of the high-precision, large-sized, and heavy platform INSs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Economical High–Low Temperature and Heading Rotation Test Method for the Evaluation and Optimization of the Temperature Control System for High-Precision Platform Inertial Navigation Systems

Yang

Zhang

2018

Self Cite

Inertial navigation systems (INSs) use the temperature control system to ensure the stability of the temperature of the inertial sensors for improving the navigation accuracy of the INSs. That is, the temperature control accuracy affects the performance of the INSs. Thus, the performance of temperature control systems must be evaluated before their application. However, nearly all high-precision INSs are large and heavy and require long-term testing under many different experimental conditions. As a result, conducting an outdoor navigation experiment, which involves high–low temperature and heading rotation tests, is time consuming, laborious, and costly for researchers. To address this issue, an economical high–low temperature and heading rotation test method for high-precision platform INSs is proposed, and an evaluation system based on this method is developed to evaluate the performance of the temperature control systems for high-precision platform INSs indoors. The evaluation system uses an acrylic chamber, exhaust fans, temperature sensors, and an air conditioner to simulate the environment temperature change. The outer gimbals of the platform INSs are utilized to simulate the heading rotation. The temperature control system of a high-precision platform INS is evaluated using the proposed evaluation method. The temperature difference of the gyros is obtained in the high–low temperature test, and the temperature fluctuation of the temperature control system is observed in the rotation test. These tests verify the effectiveness of the proposed evaluation method. Then, the corresponding optimization method for the temperature control system of this high-precision platform INS is put forward on the basis of the test results of the evaluation system. Experimental results show that the maximum temperature differences of the two gyros between high- and low-temperature tests are decreased from 1.51 °C to 0.50 °C, and the maximum temperature fluctuation value of the temperature control system is decreased from 0.81 °C to 0.27 °C after the proposed evaluation and optimization processes. Therefore, the proposed methods are cost effective and useful for evaluating and optimization of the temperature control system for INSs.