31-07-2012, 01:23 PM
attitude heading reference system (AHRS)
[attachment=29696]
Abstract:
This will involve the hardware design of the sensor assembly of AHRS, including 3-axis gyros and accelerometers, preferably MEMS based sensors. The aim of this project is to develop an engineering model for the attitude heading reference system (AHRS) for aerospace application. The gyros will provide 3-axis rate measurement and angular rotation, and accelerometers will measure the linear acceleration forces. An AHRS consists of sensors on 3-axis that provide heading, attitude and yaw information for an aircraft. AHRS are designed to replace the traditional mechanical gyroscopic flight instruments and provide better reliability and accuracy. The output of the sensors will be converted to digital signals and will be processed by a software algorithm to realize the linear and angular motions.
PRINCIPLE OF OPERATION:
An accelerometer can sense the acceleration in 3-axis , x-y and z-directions.
A gyroscope is a device that measures angular rate about a particular axis, that is rate of change of angle with time.
A magnetometer provides long term gravity and magnetic north reference.
The output data is provided in both analog and digital formats.
Data Fusion Algorithm
Figure shows the block diagram of the data fusion algorithm by using the complementary filter, where and are the estimated roll and pitch angles respectively, and and are the roll and pitch angle rates respectively, which are transformed from the angular rate measured by the gyroscopes
This algorithm fuses the data measured from the gyroscope and accelerometer triads to obtain the estimated roll and pitch angles.
ωx, ωy and ωz are the angular rates measured by the gyroscopes in body coordinate frame. The blocks labelled as “RCF” and “PCF” in Figure represent the roll and pitch complementary filters respectively.
ax, ay and az are the components of the acceleration measured by accelerometer in the body coordinate frame.
CONCLUSION:
This article described the practical approach to the development of an AHRS algorithm. Fusing information from various sensors by means of the Kalman terenables calculation of the stable information about the user device attitude. Experimental results shows that proposed algorithms are valid and fully functional.
[attachment=29696]
Abstract:
This will involve the hardware design of the sensor assembly of AHRS, including 3-axis gyros and accelerometers, preferably MEMS based sensors. The aim of this project is to develop an engineering model for the attitude heading reference system (AHRS) for aerospace application. The gyros will provide 3-axis rate measurement and angular rotation, and accelerometers will measure the linear acceleration forces. An AHRS consists of sensors on 3-axis that provide heading, attitude and yaw information for an aircraft. AHRS are designed to replace the traditional mechanical gyroscopic flight instruments and provide better reliability and accuracy. The output of the sensors will be converted to digital signals and will be processed by a software algorithm to realize the linear and angular motions.
PRINCIPLE OF OPERATION:
An accelerometer can sense the acceleration in 3-axis , x-y and z-directions.
A gyroscope is a device that measures angular rate about a particular axis, that is rate of change of angle with time.
A magnetometer provides long term gravity and magnetic north reference.
The output data is provided in both analog and digital formats.
Data Fusion Algorithm
Figure shows the block diagram of the data fusion algorithm by using the complementary filter, where and are the estimated roll and pitch angles respectively, and and are the roll and pitch angle rates respectively, which are transformed from the angular rate measured by the gyroscopes
This algorithm fuses the data measured from the gyroscope and accelerometer triads to obtain the estimated roll and pitch angles.
ωx, ωy and ωz are the angular rates measured by the gyroscopes in body coordinate frame. The blocks labelled as “RCF” and “PCF” in Figure represent the roll and pitch complementary filters respectively.
ax, ay and az are the components of the acceleration measured by accelerometer in the body coordinate frame.
CONCLUSION:
This article described the practical approach to the development of an AHRS algorithm. Fusing information from various sensors by means of the Kalman terenables calculation of the stable information about the user device attitude. Experimental results shows that proposed algorithms are valid and fully functional.