Design and implementation of an AHRS based on an adaptive complementary filter using low-cost sensors

Attitude and Heading Reference Systems (AHRS) are commonly used in the aviation industry, as well as in unmanned aerial vehicles and robot applications. The fundamental needs of a measurement system like this include being lightweight, compact, energy-efficient, and simple to set up. Due to the increase in affordable micro inertial sensors utilizing MEMS technology, developing small and inexpensive attitude measurement systems is now feasible. The term "attitude" refers to the positioning of a vehicle in space. Attitude and Heading Reference Systems (AHRS) are instruments utilized to determine the orientation of a vehicle. AHRS is constructed using frequently utilized sensors such as a gyroscope, accelerometer and magnetometer. An AHRS generally consists of sensors measuring roll, pitch, yaw, and heading, including accelerometers, gyroscopes, and magnetometers on three axes. Sensor fusion algorithms like Allan Variance applications and complementary filtering can be used in systems with AHRS to estimate attitude information accurately. Combining gyroscope, accelerometer and magnetometer sensor data with the complementary filter, roll, pitch, yaw and heading information are obtained. The sensor fusion algorithm will leverage the different strengths of each sensor to take advantage of one sensor and compensate for the limitations of the other sensor. The fundamental idea behind a complementary filter is to prioritize the reliable sensor by assigning it more significance. Allan Variance is a simple and effective method to describe and characterize different stochastic processes and their coefficients. Simple operations will obtain the characteristic curve of the Allan deviation on the sensor output, determining the types and magnitudes of errors in the data. Allan variance is a method of analyzing a data series in the time domain and can also be used to determine the intrinsic noise in a system as a function of averaging time. The primary goal of AHRS is to provide accurate, real-time data on the orientation and direction of an object by integrating and processing information from multiple sensors. This capability is vital for navigation, stabilization and control in various fields such as aviation, maritime and robotics.