Optimal-REQUEST Algorithm for Attitude Determination

Choukroun, Daniel; Bar-Itzhack, Itzhack Y.; Oshman, Yaakov

doi:10.2514/1.10337

Cited by 65 publications

(29 citation statements)

References 11 publications

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“…Algorithms based on extended and unscented Kalman filters, particle filters and MHEs [5], [6], [7], [8] have been developed to perform sensor fusion to estimate position and orientation. In particular, [9] applied the multiplicative Extended Kalman Filter (EKF) to our system and will be used as the baseline for our simulations.…”

Section: Introductionmentioning

confidence: 99%

Moving Horizon Estimation with a huber penalty function for robust pose estimation of tethered airplanes

Geebelen

Wagner

Gros

et al. 2013

2013 American Control Conference

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This paper presents a Moving Horizon Estimator (MHE) for estimating the position and orientation (pose) of moving objects, in particular for tracking tethered airplanes for Airborne Wind Energy systems. In this application, absolute pose measurements are captured by a marker based stereo vision system. These measurements are fused with measurements of the angular velocity and linear acceleration from an Inertial Measurement Unit (IMU). In our MHE, the IMU measurements in the intervals between camera frames are modeled as samples of a superposition of orthonormal polynomial basis functions. This results in a MHE formulation that requires fewer optimization variables, enabling faster solution. In order to achieve robustness to marker detection errors, a formulation based on the Huber penalty is also presented. We show that our robust MHE formulation outperforms a MHE formulation using the 2 -norm and a traditional extended Kalman filter.

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Section: Introductionmentioning

confidence: 99%

Moving Horizon Estimation with a huber penalty function for robust pose estimation of tethered airplanes

Geebelen

Wagner

Gros

et al. 2013

2013 American Control Conference

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“…The standard way to obtain heading information is by using a tri-axis magnetometer, which can measure the signal intensity of the magnetic field in three orthogonal directions. For example, they act as heading sensors in aircraft and marine navigation, and also serve as attitude sensors in satellite navigation and control system applications [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Interference-Resistant Heading Measurement for Location-Based Mobile Services

Wu¹,

Xie

Chen

et al. 2013

2013 IEEE 10th International Conference on High Performance Computing and Communications &Amp; 2013 IEEE International Conferen

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Accurate heading information is crucial for many mobile services such as navigation, autonomous vehicles, and robot applications. The standard way to obtain heading information is by using a tri-axis magnetometer, which can measure the signal intensity of the magnetic field in three orthogonal directions. However, a magnetometer is known to be highly susceptible to environmental interferences that may easily generate error up to thousands of nano-tesla, causing unacceptable errors (e.g., 40 degrees) in the direction output and rendering the mobile service useless. We present a novel design for interference resistant heading measurement using multiple magnetometers. In this design, multiple magnetometers are placed around a circle, imitating the manual rotation of a single magnetometer. This arrangement allows us to automatically calibrate the devices against external interference, using the classic ellipse fitting method. We have implemented a system consisting of a STM32F103RC microcontroller and six AKM8975 magnetic sensors. Evaluation in realistic environments shows that our design can adaptively update the calibration parameters, reducing the original heading error from 150 degrees to below 2.5 degrees, and the heading error remains stable in environments with different patterns of magnetic perturbation.

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“…426-428]). In a previous work [19], it was proposed to optimally estimate the elements of the matrix K itself, before feeding them to an eigenvector solver, and using the results of [20], the process equation for that matrix was written as a 4 £ 4 matrix equation, as follows:…”

Section: B Quaternion Estimationmentioning

confidence: 99%