Improved Attitude and Heading Accuracy with Double Quaternion Parameters Estimation and Magnetic Disturbance Rejection

Abstract

The use of unmanned aerial vehicle (UAV) applications has grown rapidly over the past decade with the introduction of low-cost microelectromechanical system (MEMS)-based sensors that measure angular velocity, gravity, and magnetic field, which are important for an object orientation determination. However, the use of low-cost sensors has also been limited because their readings are easily distorted by unwanted internal and/or external noise signals such as environmental magnetic disturbance, which lead to errors in attitude and heading estimation results. In an extended Kalman filter (EKF) process, this study proposes a method for mitigating the effect of magnetic disturbance on attitude determination by using a double quaternion parameters for representation of orientation states, which decouples the magnetometer from attitude computation. Additionally, an online measurement error covariance matrix tuning system was implemented to reject the impact of magnetic disturbance on the heading estimation. Simulation and experimental tests were conducted to verify the performance of the proposed methods in resolving the magnetic noise effect on attitude and heading. The results showed that the proposed method performed better than complimentary, gradient descent, and single quaternion-based EKF.

Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation

“…Also, q k , T s , and I 4×4 represent the current time attitude prediction in the quaternion, sampling time, and identity matrix, respectively. The derivation for Equation ( 4) can be found in [20]. Due to the Taylor series approximation and discretization process applied in driving Equation ( 4), inaccuracy in the model is inevitable.…”

“…The acquisition function considers the expected mean and variance at each point along the objective function domain to compute the value that indicates how desirable it is to sample next at that position. Suppose we would like to minimize f(x), and the best solution so far is x , then, the equation of expected improvement (EI) is defined as Equation (20).…”

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

“…Noise and Bayesian inference: Inference methods such as Bayesian probabilistic fusion, evidential belief reasoning and fuzzy logic rely on probability distribution patterns to counter the effect of uncertainty in the sensor data. Sensor performance is affected by environmental factors such as changes in the magnetic field, temperature, etc., that add uncertainty to the measured data [76,91]. (vi) Detecting overlap: Detecting overlap between individual sensor data can enhance complementary or cooperative sensor data fusion and reduce redundancy.…”

Analysis of Stochastic Properties of MEMS Accelerometers and Gyroscopes Used in the Miniature Flight Data Recorder

“…2024, 14, x FOR PEER REVIEW 2 of 23 applications are presented in the works [1][2][3][4][5]. Because of their small size, they are the only possible choice for small UAVs [6,7]. Due to their low cost, they are popular in General Aviation planes for control and data recording systems [8][9][10].…”