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

Abstract

The attitude and heading reference system (AHRS) is an important concept in the area of navigation, image stabilization, and object detection and tracking. Many studies and works have been conducted in this regard to estimate the accurate orientation of rigid bodies. In most research in this area, low-cost MEMS sensors are employed, but since the system’s response will diverge over time due to integration drift, it is necessary to apply proper estimation algorithms. A two-step extended Kalman Filter (EKF) algorithm is used in this study to estimate the orientation of an IMU. A 9-DOF device is used for this purpose, including a 6-DOF IMU with a three-axis gyroscope and a three-axis accelerometer, and a three-axis magnetometer. In addition, to have an accurate algorithm, both IMU and magnetometer biases and disturbances are modeled and considered in the real-time filter. After applying the algorithm to the sensor’s output, an accurate orientation as well as unbiased angular velocity, linear acceleration, and magnetic field were achieved. In order to demonstrate the reduction of noise power, fast Fourier transform (FFT) diagrams are used. The effect of the initial condition on the response of the system is also investigated.

Effects of nordic walking training on gait and exercise tolerance in male ischemic heart disease patients

“…The MyoMotion system, like any inertial measurement unit IMU-based system, has intrinsic limitations when compared to optoelectronic or video-based methods 23,24 . IMU systems can be susceptible to drift over time and may be affected by magnetic disturbances in the environment 24 .…”

“…The MyoMotion system, like any inertial measurement unit IMU-based system, has intrinsic limitations when compared to optoelectronic or video-based methods 23,24 . IMU systems can be susceptible to drift over time and may be affected by magnetic disturbances in the environment 24 . Moreover, while IMUs provide valuable data on segmental motion through space, they lack the spatial resolution of optoelectronic systems, which can more precisely measure the position of markers in a laboratory setting 23,25 .…”

Optimization of Gradient Descent Parameters in Attitude Estimation Algorithms

“…These techniques combine measurements from various different sensors into a single, more accurate, and reliable attitude estimate. Often, an integrated inertial measurement unit (IMU) consisting of a tri-axis accelerometer, a tri-axis magnetometer, and a tri-axis gyroscope provides a low cost, lightweight, and easily accessible measurement system [ 18 , 19 , 20 , 21 ]. Although easily accessible, these sensors have some major drawbacks that complicate the attitude estimation.…”

“…Although IMU sensors cannot provide reliable attitude estimation on their own, through sensor fusion techniques, adequate accuracy can be attained. By far the most widespread sensor fusion technique for attitude estimation used in practice is the Kalman filter [ 19 , 20 , 21 ] due to its high accuracy and efficiency. Despite the advantages of the Kalman filter, its implementation is more complex and requires higher computational power, which may not be suitable for low-power applications.…”

Evaluation of Parameter Identification of a Real Manipulator Robot

“…Localization is among the most used applications of the KF and its modifications [5][6][7]. This application is employed to correct the position of some robots [8][9][10], for trajectory tracking [11][12][13][14], and for parameter estimation [15][16][17].…”