In this study, an Attitude and Heading Reference System (AHRS) consisting of 5 modules was designed.

What is it about?

In recent years, performance of micro‐electromechanical systems (MEMS) has been improved significantly. Their application has also escalated due to their low price, small size, and low energy consumption. One of their applications is the combination of inertial measurement units (IMUs), consisting of accelerometers and gyroscopes, with magnetometers for building an Attitude and Heading Reference System (AHRS). By measuring the orientation of a moving object, AHRS is utilized in numerous applications, including mobile robots, unmanned aerial vehicles (UAVs), autonomous underwater vehicles (AUVs), human motion tracking, and autonomous robot control.

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Why is it important?

Gyroscopes measure the rotation rate. Integrating this rate will give us the object's current orientation. A disadvantage of MEMS sensors is the existence of error and high‐frequency noise in their output. Considering these error accumulationduring integration, orientation error increases as time passes. Accelerometers measure the Earth's gravitational vector in their static state. Using this vector, one can prevent the divergence of roll and pitch angles; however, it is of no help in converging the heading angle. To solve this problem, one can use a magnetometer which measures the magnetic field of the Earth. Nevertheless, in addition to the mentioned errors,the soft‐iron and hard‐iron effects also distort the measurement. Therefore, before using MEMS sensors, they must be calibrated to eliminate the errors. In this study, an Attitude and Heading Reference System (AHRS) consisting of 5 modules is designed where each module has a triaxial gyroscope, accelerometer, and magnetometer. First, a method based on the Levenberg‐Marquardt algorithm (LMA) is utilized to correct the bias error, scale factor and axes nonorthogonality. Also, the data from the 5 modules of AHRS are ensemble averaged to reduce the adverse effects of high‐frequency noises. Then, the obtained trends are used in an orientation estimation algorithm based on a complementary filter algorithm.