An Attitude Filtering and Magnetometer Calibration Approach for Nanosatellites

Söken, Halil Ersin

doi:10.1007/s42405-018-0020-8

Cited by 12 publications

(4 citation statements)

References 21 publications

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“…''Hard iron'' error and ''Soft iron'' error can be obtained by taking a set of samples that are assumed to be the product of rotation in Earth's magnetic field and fitting an offset ellipsoid to them, determining the correction to be applied to adjust the samples into an origin-centered sphere. 17 The L-M algorithm for sphere and ellipsoid fitting…”

Section: Error Source Of Magnetometermentioning

confidence: 99%

An improved magnetometer calibration and compensation method based on Levenberg–Marquardt algorithm for multi-rotor unmanned aerial vehicle

Pei

et al. 2020

Measurement and Control

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In order to improvethe yaw angle accuracy of multi-rotor unmanned aerial vehicle and meet the requirement of autonomous flight, a new calibration and compensation method for magnetometer based on Levenberg–Marquardt algorithm is proposed in this paper. A novel mathematical calibration model with clear physical meaning is established. “Hard iron” error and “Soft iron” error of magnetometer which affect the yaw accuracy of unmanned aerial vehicle are compensated. Initially, Levenberg–Marquardt algorithm is applied to the process of sphere fitting for the original magnetometer data; the optimal estimation of sphere radius and initial “Hard iron” error are obtained. Then, the ellipsoid fitting is performed, and the optimal estimation of “Hard iron” error and “Soft iron” error are obtained. Finally, the calibration parameters are used to compensate for the magnetometer’s output during unmanned aerial vehicle flight. Traditional ellipsoid fitting based on least squares algorithm is taken as reference to prove the effectiveness of the proposed algorithm. Semi-physical simulation experiment proves that the proposed magnetometer calibration method significantly enhances the accuracy of magnetometer. Static test shows that the yaw angle error is reduced from 1.2° to 0.4° when using the proposed calibration model to calibrate magnetometers. In dynamic tests, the sensor MTi’s output is used as reference. The data fusion of magnetometer compensated by the proposed new calibration model based on Levenberg–Marquardt algorithm can accurately track the desired attitude angle. Experimental results indicate that the accuracy of magnetometer in the yaw angle estimation has been greatly enhanced. In the process of attitude estimated, the compensation magnetometer data given by this new method have faster convergence speed, higher accuracy, and better performance than the compensation magnetometer data given by traditional ellipsoid fitting based on least squares algorithm.

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Section: Error Source Of Magnetometermentioning

confidence: 99%

An improved magnetometer calibration and compensation method based on Levenberg–Marquardt algorithm for multi-rotor unmanned aerial vehicle

Pei

et al. 2020

Measurement and Control

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“…Estimation accuracy factors, increasing available computing power, reducing the size and cost of the spacecraft model have played an important role in using these more sophisticated algorithms. Today, the most common technique for determining the attitude of Kalman filters is estimate the spacecraft's attitude [2,5]. Sequential filters are also used to determine the attitude.…”

Section: Methods Determining the Attitude Of The Nanosatellite Systemmentioning

confidence: 99%

“…Assuming that the optimal estimates for the state 𝑥 (𝑘−1) 𝑎 and the error covariance 𝑃 (k−1) are available in step k-1, a prediction is made for the state in step k; this prediction using Measurement data is modified. The prediction phase is obtained with (5).…”

Section: A Extended Kalman Filter Designmentioning

confidence: 99%

Extended Kalman Filter Design to Estimate the Attitude of the Nanosatellite System Using Magnetometer

Silva,

McKell,

Parker

2023

Control Syst. Optim. Lett.

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The problem of estimating nonlinear systems is an important issue in many engineering applications. One of these applications is in the nanosatellite for Vehicle-to-everything (V2X) communications, which is used to replace sensors in the event of failure or error. Therefore, the accuracy of pre-filter estimation is of great importance. To control the orientation of a satellite, it is important to estimate the attitude accurately. Time-series estimation is especially important in micro and nanosatellites, whose sensors are usually low-cost and have higher noise levels than high-end sensors. Also, the algorithms should be able to run on systems with very restricted computer power. In this paper, an overview of the algorithms used to determine the attitude of nanosatellites, and especially using only the magnetometer sensor, will be discussed. This paper aims to simulate a nanosatellite system to estimate the magnetic field derivative vector using only the magnetometer data with the desired accuracy. The estimated vectors in the pre-filter, along with the magnetometer sensor data, are used to estimate the attitude of the satellite. The estimation of the state vector consisting of the vector part of quaternion and the angular velocity vector is used for the control calculation. To evaluate the accuracy of this pre-filter, a comparison of the magnetometer vector estimator with the body sensor data has been used.

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“…A magnetic torquer is a simple current carrying coil that can generate the desired magnetic moment [4]. The torquer is mounted on the satellite and the interaction between the magnetic moments of the torquer and the Earth's magnetic field produces a torque on the satellite.…”

Section: Introductionmentioning

confidence: 99%

A New Active Control Driver Circuit for Satellite’s Torquer System Using Second Generation Current Conveyor

et al. 2021

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In this article a new active control driver circuit is designed using the second-generation current conveyor for the satellite’s torquer system. The torquer plays an important role in the attitude control of the satellite. Based on the magneto-meter data, the satellite’s microprocessor calculates the required current for the torque and sends a reference command. A close loop control system is designed, which generates the desired output current. The parameters of the controller are optimized using a variant of the well-known evolutionary algorithm, the genetic algorithm (GA). This variant is known as the segmented GA. The controller is experimentally implemented using the commercially available integrated circuit, the AD844. The error between the experimental and simulation results has RMS values in range of 0.01–0.16 A for the output current and 0.41–0.6 V for the output voltage. It has mean value of 0.01 A for the output current and has mean values in the range of 0.33–0.48 V for the output voltage. It has standard deviation of 0.01 A for the output current and standard deviations in the range of 0.24–0.35 V for the output voltage. Thus, there is a close match between the simulation and experimental results, validating the design approach. These designs have many practical applications, particularly for nanosatellites powered by photovoltaic panels.

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An Attitude Filtering and Magnetometer Calibration Approach for Nanosatellites

Cited by 12 publications

References 21 publications

An improved magnetometer calibration and compensation method based on Levenberg–Marquardt algorithm for multi-rotor unmanned aerial vehicle

An improved magnetometer calibration and compensation method based on Levenberg–Marquardt algorithm for multi-rotor unmanned aerial vehicle

Extended Kalman Filter Design to Estimate the Attitude of the Nanosatellite System Using Magnetometer

A New Active Control Driver Circuit for Satellite’s Torquer System Using Second Generation Current Conveyor

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