Noise Characteristics of MEMS Gyro’s Null Drift and Temperature Compensation Jaw-Kuen Shiau*, Chen-Xuan Huang and Ming-Yu Chang Department of Aerospace Engineering, Tamkang University, Tamsui, Taiwan 251, R.O.C. Abstract Gyroscope is one of the primary sensors for air vehicle navigation and controls. This paper investigates the noise characteristics of microelectromechanical systems (MEMS) gyroscope null drift and temperature compensation. This study mainly focuses on temperature as a long-term error source. An in-house-designed inertial measurement unit (IMU) is used to perform temperature effect testing in the study. The IMU is placed into a temperature control chamber. The chamber temperature is controlled to increase from 25 °C to 80 °C at approximately 0.8 degrees per minute. After that, the temperature is decreased to -40 °C and then returns to 25 °C. The null voltage measurements clearly demonstrate the rapidly changing short-term random drift and slowly changing long-term drift due to temperature variations. The characteristics of the short-term random drifts are analyzed and represented in probability density functions. A temperature calibration mechanism is established by using an artificial neural network to compensate the long-term drift. With the temperature calibration, the attitude computation problem due to gyro drifts can be improved significantly. Key Words: MEMS Gyroscope, Null Drift, Inertial Measurement Unit, Temperature Compensation 1. Introduction The effectiveness of navigation and controls of an air vehicle are highly dependent on the degree of pre- cision of the on-board inertial measurement unit (IMU). A gyroscope is one of the primary sensors that comprises the IMU. Traditional units, such as gimbaled gyroscopes, laser gyroscopes, and fiber-optic gyroscopes provide high-precision angular rate information for navigation and control systems [1]. However, they are expensive and bulky. With the maturation and advancement of semi-conductor manufacturing technology, MEMS ap- plication has shifted to non-military purposes in the re- gular consumer market. As MEMS sensors become cheaper, smaller, and consume less power, they are in- creasingly used in flight attitude calculations of un- manned aerial vehicles (UAVs) [2,3]. This study uses the low-cost MEMS IMU unit as the basis of this research. Due to manufacturing limitations, signal drift often accompanies MEMS gyros. As the calculation of atti- tude angles requires angular velocity measurements for integral calculation, the error caused by MEMS gyro drift increases with time, leading to decreased accuracy. Thus, drift data processing is extremely important. Gyro drift is caused by multiple factors and has complex non- linear characteristics. Therefore, it cannot be clearly ex- pressed through a single function. Due to various error sources, the performance characteristics of low-cost gy- roscopes cause usage limitations [4-7]. According to the spectral signature, these errors can be categorized into the more rapidly changing short-term noise and more slowly changing long-term noise [7]. These error sources cause basic limitations to solely using gyroscopes for attitude determination [8,9]. Several methods may be used to process drift-signals to remove short-term error. However, they are complex Journal of Applied Science and Engineering, Vol. 15, No. 3, pp. 239-246 (2012) 239 *Corresponding author. E-mail: [email protected]
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Noise Characteristics of MEMS Gyro’s Null Drift and
Temperature Compensation
Jaw-Kuen Shiau*, Chen-Xuan Huang and Ming-Yu Chang
Department of Aerospace Engineering, Tamkang University,
Tamsui, Taiwan 251, R.O.C.
Abstract
Gyroscope is one of the primary sensors for air vehicle navigation and controls. This paper
investigates the noise characteristics of microelectromechanical systems (MEMS) gyroscope null drift
and temperature compensation. This study mainly focuses on temperature as a long-term error source.
An in-house-designed inertial measurement unit (IMU) is used to perform temperature effect testing in
the study. The IMU is placed into a temperature control chamber. The chamber temperature is
controlled to increase from 25 �C to 80 �C at approximately 0.8 degrees per minute. After that, the
temperature is decreased to -40 �C and then returns to 25 �C. The null voltage measurements clearly
demonstrate the rapidly changing short-term random drift and slowly changing long-term drift due to
temperature variations. The characteristics of the short-term random drifts are analyzed and
represented in probability density functions. A temperature calibration mechanism is established by
using an artificial neural network to compensate the long-term drift. With the temperature calibration,
the attitude computation problem due to gyro drifts can be improved significantly.
Key Words: MEMS Gyroscope, Null Drift, Inertial Measurement Unit, Temperature Compensation
1. Introduction
The effectiveness of navigation and controls of an
air vehicle are highly dependent on the degree of pre-
cision of the on-board inertial measurement unit (IMU).
A gyroscope is one of the primary sensors that comprises
the IMU. Traditional units, such as gimbaled gyroscopes,
laser gyroscopes, and fiber-optic gyroscopes provide
high-precision angular rate information for navigation
and control systems [1]. However, they are expensive
and bulky. With the maturation and advancement of
semi-conductor manufacturing technology, MEMS ap-
plication has shifted to non-military purposes in the re-
gular consumer market. As MEMS sensors become
cheaper, smaller, and consume less power, they are in-
creasingly used in flight attitude calculations of un-
manned aerial vehicles (UAVs) [2,3]. This study uses the
low-cost MEMS IMU unit as the basis of this research.
Due to manufacturing limitations, signal drift often
accompanies MEMS gyros. As the calculation of atti-
tude angles requires angular velocity measurements for
integral calculation, the error caused by MEMS gyro
drift increases with time, leading to decreased accuracy.
Thus, drift data processing is extremely important. Gyro
drift is caused by multiple factors and has complex non-
linear characteristics. Therefore, it cannot be clearly ex-
pressed through a single function. Due to various error
sources, the performance characteristics of low-cost gy-
roscopes cause usage limitations [4�7]. According to the
spectral signature, these errors can be categorized into
the more rapidly changing short-term noise and more
slowly changing long-term noise [7]. These error sources
cause basic limitations to solely using gyroscopes for
attitude determination [8,9].
Several methods may be used to process drift-signals
to remove short-term error. However, they are complex
Journal of Applied Science and Engineering, Vol. 15, No. 3, pp. 239�246 (2012) 239