Integrated calibration of magnetic gradient tensor system

Yin, Gang; Zhang, Yingtang; Fan, Hongbo; Ren, Guoyu; Li, Zhining

doi:10.1016/j.jmmm.2014.08.022

Cited by 15 publications

(3 citation statements)

References 19 publications

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“…Li [21] introduced Genetic Algorithm (GA) into the SR system to optimize parameters automatically which was used for on-line detection of blade crack defects. Yin [22,23] established a cross-shaped vector sensor array for Tensor Fusion (TF) and correction to achieve high-precision target positioning.…”

Section: Introductionmentioning

confidence: 99%

Metrology and Measurement Systems

Zhao,

Wang,

Chen

et al. 2020

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In order to find the defects in ferromagnetic materials, a non-contact harmonic detection method is proposed. According to the principle of frequency modulated carrier wave, a tunnel magneto resistance harmonic focusing vector array detector was designed which radiates lower and higher frequency electromagnetic waves through the coil array to the detection targets. We use bistable stochastic resonance to enhance the energy of collected weak target signal and apply quantum computation and a Sobol low deviation sequence to improve genetic algorithm performance. Then we use the orthogonal phase-locked loop to eliminate the intrinsic background excitation field and tensor calculations to fuse the vector array signal. The finite element model of array detector and the magnetic dipole harmonic numerical model were also established. The simulation results show that the target signal can be identified effectively, its focusing performance is improved by 2 times, and the average signal-to-noise ratio is improved by 9.6 times after the algorithm processing. For the experiments, we take Q235 steel pipeline as the object to realize the recognition of three defects. Compared with the traditional methods, the proposed method is more effective for ferromagnetic materials defects detection.

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Section: Introductionmentioning

confidence: 99%

Metrology and Measurement Systems

Zhao,

Wang,

Chen

et al. 2020

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“…This problem is also investigated in a variety of 2D cases in which fitting a quadratic surface to a set of data is desired [1][2][3][4][5]. Quadratic surfaces almost fit every set of 3D data [6] which have a wide range of application such as 3D reconstruction [7], pose estimation [8], restricted stereo correspondence problem [9] and object recognition analysis and sensor calibration [10][11][12][13]. One of the most important applications of ellipsoid fitting in sensor calibration is three-axis magnetic sensor calibration [10].…”

Section: Introductionmentioning

confidence: 99%

“…Quadratic surfaces almost fit every set of 3D data [6] which have a wide range of application such as 3D reconstruction [7], pose estimation [8], restricted stereo correspondence problem [9] and object recognition analysis and sensor calibration [10][11][12][13]. One of the most important applications of ellipsoid fitting in sensor calibration is three-axis magnetic sensor calibration [10]. These sensors are widely used in navigation systems to measure the intensity of the earth magnetic field [12].…”

Section: Introductionmentioning

confidence: 99%

Robust ellipsoid fitting method based on optimization of a novel nonlinear cost function in navigation systems

Mirzaei

Hosseini

2019

J Braz. Soc. Mech. Sci. Eng.

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Low-cost sensors based on micro-electro-mechanical systems (MEMS) are typically used for attitude determination in navigation systems especially magnetometer which is used for heading determination. The measured value of the MEMS magnetometer is subjected to different kinds of error such as random noise, constant bias, non-orthogonality, scale factor deviation and more importantly hard iron and soft iron effects. Therefore, in order to reach more accurate measurement, highprecision calibration is needed. One of the most common methods for calibrating MEMS magnetic sensors is least squares ellipsoid fitting. But, the common least squares ellipsoid fitting method can be inefficient for real-time applications in the presence of colored noise and outliers. In this paper, a modified ellipsoid fitting method is proposed in which a nonlinear optimization is developed to minimize a novel cost function. In the cost function of the proposed robust method, the effect of outliers and noise is considered and the standard deviation of the data is kept minimum. Finally, the efficiency of the new algorithms is demonstrated through the experimental results.

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