The Attitude Heading Reference System (AHRS) has been widely used to provide the position and orientation of a rigid body. A low cost MEMS based inertial sensor measurement unit (IMU) is a core device in AHRS. To improve the AHRS system performance, there is a need to develop (i) stochastic IMU error models and (ii) random noise minimization techniques. In this paper, we modify the Sage-Husa Adaptive Kalman Filter (SHAKF) to incorporate time-varying noise estimator and robustifier, termed as Modified Sage-Husa Adaptive Robust Kalman Filter (MSHARKF). In the proposed algorithm, a three segment approach is used to evaluate the adaptive scale factor followed by the learning statistics. The scale factor is iteratively updated in the MSHARKF equations. In addition, angle random walk (ARW) and bias instability (BI) errors are represented by state-space models. The proposed algorithm is applied to restrain the drift error and random noise in the MEMS IMUs signals. The performance of this algorithm is analyzed using Allan variance (AV) analysis for static signals whereas the Root Mean Square Error (RMSE) values are evaluated for dynamic signals. Experimental results demonstrate the effectiveness of MSHARKF in reducing the drift and random noise in static and dynamic conditions as compared with other existing algorithms. Finally, we present sufficient conditions for convergence proof of the MSHARKF algorithm.

MEMS-Based IMU Drift Minimization: Sage Husa Adaptive Robust Kalman Filtering

Multiple Model Adaptive Complementary Filter for Attitude Estimation

Accurate attitude and heading reference system (AHRS) play an essential role in navigation applications and human body tracking systems. Using low-cost microelectromechanical system (MEMS) inertial sensors and having accurate orientation estimation, simultaneously, needs optimum orientation methods and algorithms. The error of attitude estimation may lead to imprecise navigation and motion capture results. This paper proposed a novel intermittent calibration technique for MEMS-based AHRS using error prediction and compensation filter. The method, inspired from the recognition of gyroscope's error and by a proportional integral (PI) controller, can be regulated to increase the accuracy of the prediction. The experimentation of this study for the AHRS algorithm, aided by the proposed prediction filter, was tested with real low-cost MEMS sensors consists of accelerometer, gyroscope, and magnetometer. Eventually, the error compensation was performed by post-processing the measurements of static and dynamic tests. The experimental results present about 35% accuracy improvement in attitude estimation and demonstrate the explicit performance of proposed method.

https://www.mdpi.com/1424-8220/20/14/4055/pdf

Accuracy Improvement of Attitude Determination Systems Using EKF-Based Error Prediction Filter and PI Controller.

This paper presents sensor fusion algorithm using nonlinear complementary filter (NCF) for attitude estimation and a proportional-integral-derivative (PID) controller for small-scale quadrotor unmanned aerial vehicle (UAV) stabilization. From inertial measurement unit (IMU) data, gyroscope as a main sensor is fused to another two sensors; accelerometer and magnetometer to correct drift error of gyroscope to obtain reliable attitude estimation. In this paper, the performance of NCF is implemented on real-time while applying PID controller for attitude stabilization of quadrotor UAV during hovering. Experimental results show the effectiveness of PID controller for quadrotor UAV stabilization during attitude control.

/pdf/sensor-fusion-for-attitude-estimation-and-pid-control-of-4uulg9n821.pdf

Sensor Fusion for Attitude Estimation and PID Control of Quadrotor UAV

This paper proposes a sensor fusion algorithm by complementary filter technique for attitude estimation of quadrotor UAV using low-cost MEMS IMU. Angular rate from gyroscope tend to drift over a time while accelerometer data is commonly effected with environmental noise. Therefore, high frequency gyroscope signal and low frequency accelerometer signal is fused using complementary filter algorithm. The complementary filter scaling factor K1=0.98 and K2=0.02 are used to merge both gyro and accelerometer. The results show that the smooth roll, pitch and yaw attitude angle can be obtained from the low cost IMU by using proposed sensor fusion algorithm.

Sensor Fusion Algorithm by Complementary Filter for Attitude Estimation of Quadrotor with Low-Cost IMU

This paper presents design and implementation of an attitude and heading reference system (AHRS) based on low-cost MEMS sensors and complementary filtering (CF). Different from traditional solutions, information fusion is performed with Euler angles directly, which is more straightforward for understanding; however it proposes many challenges for reaching a stable and accurate estimation as when these angles approach or traverse their range boundaries, estimation may get discontinuous. Thus an effective discontinuity avoiding strategy is suggested in this paper to refine the estimation. Besides, instead of extended Kalman filtering (EKF), CF is utilized for state estimation of AHRS as it features fusion of high-frequency and low-frequency signals. In order to make up for shortcomings of MEMS sensors such as multiple errors, drifts, and bad accuracy, some effective calibration and filtering algorithms are proposed to guarantee agreeable AHRS performance. Also, architecture of the MEMS IMU (inertial measure...

https://journals.sagepub.com/doi/pdf/10.1155/2014/214726

Design and Implementation of an AHRS Based on MEMS Sensors and Complementary Filtering

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Design and Implementation of an AHRS Based on MEMS Sensors and Complementary Filtering