9-DOF IMU-Based Attitude and Heading Estimation Using an Extended Kalman Filter with Bias Consideration

Area of Science:

• Robotics and Control Systems
• Sensor Fusion and Navigation

Background:

• Attitude and Heading Reference Systems (AHRS) are crucial for navigation, image stabilization, and object tracking.
• Low-cost MEMS sensors are commonly used but suffer from integration drift, necessitating advanced estimation algorithms.
• Accurate orientation estimation of rigid bodies is a persistent challenge in various technological applications.

Purpose of the Study:

• To develop and validate a robust orientation estimation algorithm for Inertial Measurement Units (IMUs).
• To address and mitigate the issue of integration drift inherent in MEMS sensor data.
• To improve the accuracy of orientation estimation in real-time applications.

Main Methods:

• Implementation of a two-step extended Kalman Filter (EKF) algorithm.
• Utilizing a 9-Degrees-of-Freedom (DOF) device comprising a 6-DOF IMU (gyroscope, accelerometer) and a magnetometer.
• Modeling and incorporating IMU and magnetometer biases and disturbances into the real-time filter.

Main Results:

• Achieved accurate orientation estimation for the IMU.
• Successfully obtained unbiased angular velocity, linear acceleration, and magnetic field measurements.
• Demonstrated noise power reduction using Fast Fourier Transform (FFT) analysis.

Conclusions:

• The proposed two-step EKF algorithm effectively estimates orientation and corrects sensor drift.
• The algorithm provides accurate and unbiased sensor data, crucial for navigation and stabilization.
• Further investigation into the impact of initial conditions on system performance was conducted.