Magnetic Field Sensor Calibration for Attitude Determination

Klingbeil, Lasse; Eling, Christian; Zimmermann, Florian; Kuhlmann, Heiner

doi:10.1515/jag-2014-0003

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…However, the main difficulties with magnetic field sensors are their sensitivities to ferromagnetic material in the vicinity of the sensor. In case this material is part of the sensor platform, disturbances can be compensated by calibration [ 15 ]. Nevertheless, the changing currents of the rotors or ferromagnetic material in the environment can still lead to distortions of the magnetic field observations.…”

Section: The Sensor Integrationmentioning

confidence: 99%

“…However, the main problem when using a magnetometer on a UAV is the distortion of the magnetic field measurements, caused by various time-constant and time-varying effects. Although most of the constant effects, coming from ferromagnetic material in the vicinity of the sensor, can be compensated by appropriate calibration methods [ 14 , 15 ], the non-constant effects, e.g., high electrical currents during the operation of rotary-wing UAVs, inhibit sub-degree attitude accuracies in most cases.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Single-Frequency GPS/MEMS-IMU Attitude Determination of Lightweight UAVs

Eling

Klingbeil

Kuhlmann

2015

Sensors

Self Cite

102

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In this paper, a newly-developed direct georeferencing system for the guidance, navigation and control of lightweight unmanned aerial vehicles (UAVs), having a weight limit of 5 kg and a size limit of 1.5 m, and for UAV-based surveying and remote sensing applications is presented. The system is intended to provide highly accurate positions and attitudes (better than 5 cm and 0.5∘) in real time, using lightweight components. The main focus of this paper is on the attitude determination with the system. This attitude determination is based on an onboard single-frequency GPS baseline, MEMS (micro-electro-mechanical systems) inertial sensor readings, magnetic field observations and a 3D position measurement. All of this information is integrated in a sixteen-state error space Kalman filter. Special attention in the algorithm development is paid to the carrier phase ambiguity resolution of the single-frequency GPS baseline observations. We aim at a reliable and instantaneous ambiguity resolution, since the system is used in urban areas, where frequent losses of the GPS signal lock occur and the GPS measurement conditions are challenging. Flight tests and a comparison to a navigation-grade inertial navigation system illustrate the performance of the developed system in dynamic situations. Evaluations show that the accuracies of the system are 0.05∘ for the roll and the pitch angle and 0.2∘ for the yaw angle. The ambiguities of the single-frequency GPS baseline can be resolved instantaneously in more than 90% of the cases.

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Section: The Sensor Integrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Single-Frequency GPS/MEMS-IMU Attitude Determination of Lightweight UAVs

Eling

Klingbeil

Kuhlmann

2015

Sensors

Self Cite

102

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“…This causes bias in magnetometer outputs; hence it is necessary to calibrate the magnetometer. The commonly used calibration method is ellipsoid fitting [36,37].…”

Section: Heading Estimation By Magnetometermentioning

confidence: 99%

A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model

2019

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With the popularity of smartphones and the development of microelectromechanical system (MEMS), the pedestrian dead reckoning (PDR) algorithm based on the built-in sensors of a smartphone has attracted much research. Heading estimation is the key to obtaining reliable position information. Hence, an adaptive Kalman filter (AKF) based on an autoregressive model (AR) is proposed to improve the accuracy of heading for pedestrian dead reckoning in a complex indoor environment. Our approach uses an autoregressive model to build a Kalman filter (KF), and the heading is calculated by the gyroscope, obtained by the magnetometer, and stored by previous estimates, then are fused to determine the measurement heading. An AKF based on the innovation sequence is used to adaptively adjust the state variance matrix to enhance the accuracy of the heading estimation. In order to suppress the drift of the gyroscope, the heading calculated by the AKF is used to correct the heading calculated by the outputs of the gyroscope if a quasi-static magnetic field is detected. Data were collected using two smartphones. These experiments showed that the average error of two-dimensional (2D) position estimation obtained by the proposed algorithm is reduced by 40.00% and 66.39%, and the root mean square (RMS) is improved by 43.87% and 66.79%, respectively, for these two smartphones.

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“…However, achieving real-time localization with invariants requires a complex magnetic gradient tensor measurement system structure, involving a considerable number of magnetic fluxgate sensors. This complexity results in a higher number of error parameters to be corrected [25][26][27][28], posing challenges in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Two-Point Localization Algorithm of a Magnetic Target Based on Tensor Geometric Invariant

Chi,

Wang,

Tao

et al. 2024

Sensors

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Currently, magnetic gradient tensor-based localization methods face challenges such as significant errors in geomagnetic field estimation, susceptibility to local optima in optimization algorithms, and inefficient performance. In addressing these issues, this article propose a two-point localization method under the constraint of overlaying geometric invariants. This method initially establishes the relationship between the target position and the magnetic gradient tensor by substituting an intermediate variable for the magnetic moment. Exploiting the property of the eigenvector corresponding to the minimum absolute eigenvalue being perpendicular to the target position vector, this constraint is superimposed to formulate a nonlinear system of equations of the target’s position. In the process of determining the target position, the Nara method is employed for obtaining the initial values, followed by the utilization of the Levenberg–Marquardt algorithm to derive a precise solution. Experimental validation through both simulations and experiments confirms the effectiveness of the proposed method. The results demonstrate its capability to overcome the challenges faced by a single-point localization method in the presence of some errors in geomagnetic field estimation. In comparison to traditional two-point localization methods, the proposed method exhibits the highest precision. The localization outcomes under different noise conditions underscore the robust noise resistance and resilience of the proposed method.

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Magnetic Field Sensor Calibration for Attitude Determination

Cited by 7 publications

References 12 publications

Real-Time Single-Frequency GPS/MEMS-IMU Attitude Determination of Lightweight UAVs

Real-Time Single-Frequency GPS/MEMS-IMU Attitude Determination of Lightweight UAVs

A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model

Two-Point Localization Algorithm of a Magnetic Target Based on Tensor Geometric Invariant

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