Error Calibration of Tri-axial Magnetometer Based on Particle Swarm Optimization Algorithm

Wu, Fengxi; Hua, Bing; Kang, Guohua

doi:10.1007/978-3-642-54740-9_50

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…A particle swarm optimization (PSO) algorithm-based error calibration method is presented in [14], where the calculation of bias and scale factor errors has been performed. The errors have been significantly reduced, and the simulations showed fast convergence speed and high accuracy.…”

Section: Magnetometermentioning

confidence: 99%

Evolutionary algorithm based 9DOF sensor board calibration

Šarčević

Pletl

Kincses

2014

2014 IEEE 12th International Symposium on Intelligent Systems and Informatics (SISY)

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Accelerometers, gyroscopes and magnetometers can be used in a large variety of applications, and their calibration is a very actual problem due to high error rates, especially when errors are integrated in time. Systematic errors from the measurement values can be removed by sensor calibration, thus the applicability of the sensor can be increased. In this paper, a new evolutionary algorithmbased, quick and easy-to-use calibration method is presented. The algorithm has been developed and tested with real measurement data of the above-mentioned sensors. During this work, measurement data have been collected with 9 degree of freedom (9DOF) sensor boards, which are built up of three-axis accelerometer, gyroscope and magnetometer. For accelerometer and magnetometer calibration, bias values, scale factors and non-orthogonality corrections have been calculated, while for the gyroscopes only offsets have been determined.

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Section: Magnetometermentioning

confidence: 99%

Evolutionary algorithm based 9DOF sensor board calibration

Šarčević

Pletl

Kincses

2014

2014 IEEE 12th International Symposium on Intelligent Systems and Informatics (SISY)

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“…The traditional magnetic measurement tool for geomagnetic navigation is the three-axis magnetometer [13,14], which is affected by processing accuracy and cannot fully achieve pairwise orthogonality in the three detection directions. To compensate for the error of three-axis magnetometers, mathematical methods such as least squares method [15,16], particle swarm optimization algorithm [17,18] are usually used. However, these methods can to some extent correct the errors, and the revised measurement accuracy still needs further improvement.…”

Section: Introductionmentioning

confidence: 99%

Application of bioinspired geomagnetic sensor measurements and geomagnetic map modeling based on neural networks in simulated navigation

Shi,

Tang,

Wang

et al. 2024

Meas. Sci. Technol.

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A geomagnetic field is a vector field in which the strength and direction are related to geographical location. Geomagnetic navigation technology, which uses collected geomagnetic field information to achieve positioning and navigation, has the advantages of reliability, stability, accuracy, and concealment. With the deepening research on geomagnetic navigation, bioinspired geomagnetic navigation technology has also been developed, which mainly studies and imitates the magnetic sensing mechanism and navigation behavior of animals, providing new research ideas for geomagnetic navigation technology. The magnetic particle hypothesis and free radical pair hypothesis are two mainstream mechanisms of biological sensing using the geomagnetic field, and studies have shown that these two mechanisms may be coupled within organisms. In this study, we propose a bioinspired weak magnetic vector (BWMV) sensor based on the joint sensing mechanism of magnetic particles and free radicals. It consists of a magnetic rod made of soft magnetic material and a tunnel magnetoresistance (TMR) sensor array. A magnetic rod was used to simulate magnetic particles to convert magnetic field angle information into magnetic field intensity distribution information, and the TMR sensor array was used to simulate the perception of the magnetic field distribution by free radicals. In addition, artificial neural networks (ANNs) were used for BWMV sensors to obtain the mapping relationship between the magnetic field distribution and parameters, which can be used for geomagnetic navigation. To verify the navigation effect of the BWMV sensor in the laboratory, a simulated geomagnetic navigation device was built, and the high-precision mapping relationship from geomagnetic parameters to latitude and longitude information of the selected navigation area was obtained through another ANN. Finally, the effectiveness of the BWMV sensor based on ANNs for geomagnetic navigation is verified using simulated navigation experiments.

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“…The heading error of the magnetoresistive sensor is reduced to 2 per cent. Wu et al (2014) and Wu et al (2013) used particle swarm optimization (PSO) and extended particle swarm optimization (EPSO) algorithms to estimate the calibration parameters to improve the accuracy and robustness of the magnetic sensor calibration. The above studies have investigated various methods to improve the accuracy of ellipsoidal fitting, but the ellipsoidal equation has only nine independent coefficients, and it is impossible to determine 12 parameters of the error model.…”

Section: Introductionmentioning

confidence: 99%

Calibration of magnetic compass using an improved extreme learning machine based on reverse tuning

Liu

Fang

Shi³

2019

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Purpose The sources of magnetic sensors errors are numerous, such as currents around, soft magnetic and hard magnetic materials and so on. The traditional methods mainly use explicit error models, and it is difficult to include all interference factors. This paper aims to present an implicit error model and studies its high-precision training method. Design/methodology/approach A multi-level extreme learning machine based on reverse tuning (MR-ELM) is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. To ensure the real-time performance of the algorithm, the network structure is fixed to two ELM levels, and the maximum number of levels and neurons will not be continuously increased. The parameters of MR-ELM are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time can still be guaranteed. Findings The results show that the training time of the MR-ELM is 19.65 s, which is about four times that of the fixed extreme learning algorithm, but training accuracy and generalization performance of the error model are better. The heading error is reduced from the pre-compensation ±2.5° to ±0.125°, and the root mean square error is 0.055°, which is about 0.46 times that of the fixed extreme learning algorithm. Originality/value MR-ELM is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. In this case, the multi-level ELM network parameters are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time training can still be guaranteed. The revised manuscript improved the ELM algorithm itself (referred to as MR-ELM) and bring new ideas to the peers in the magnetic compass error compensation field.

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Error Calibration of Tri-axial Magnetometer Based on Particle Swarm Optimization Algorithm

Cited by 3 publications

References 2 publications

Evolutionary algorithm based 9DOF sensor board calibration

Evolutionary algorithm based 9DOF sensor board calibration

Application of bioinspired geomagnetic sensor measurements and geomagnetic map modeling based on neural networks in simulated navigation

Calibration of magnetic compass using an improved extreme learning machine based on reverse tuning

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