Artificial Vector Calibration Method for Differencing Magnetic Gradient Tensor Systems

• Published in: Sensors (Basel, Switzerland) Switzerland), 2018-01, Vol.18 (2), p.361
• Main Authors: Li, Qingzhu, Li, Zhining, Zhang, Yingtang, Yin, Gang
• Format: Article
• Language: English
• Subjects: artificial reference; Calibration; Computer simulation; Coordinates; Error analysis; Error detection; least-squares method; Linear equations; magnetic gradient tensor system; Misalignment; Parameter estimation; Root-mean-square errors; Sensor arrays; Sensors; Variation; vector calibration
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18020361

The measurement error of the differencing (i.e., using two homogenous field sensors at a known baseline distance) magnetic gradient tensor system includes the biases, scale factors, nonorthogonality of the single magnetic sensor, and the misalignment error between the sensor arrays, all of which can severely affect the measurement accuracy. In this paper, we propose a low-cost artificial vector calibration method for the tensor system. Firstly, the error parameter linear equations are constructed based on the single-sensor's system error model to obtain the artificial ideal vector output of the platform, with the total magnetic intensity (TMI) scalar as a reference by two nonlinear conversions, without any mathematical simplification. Secondly, the Levenberg-Marquardt algorithm is used to compute the integrated model of the 12 error parameters by nonlinear least-squares fitting method with the artificial vector output as a reference, and a total of 48 parameters of the system is estimated simultaneously. The calibrated system outputs along the reference platform-orthogonal coordinate system. The analysis results show that the artificial vector calibrated output can track the orientation fluctuations of TMI accurately, effectively avoiding the "overcalibration" problem. The accuracy of the error parameters' estimation in the simulation is close to 100%. The experimental root-mean-square error (RMSE) of the TMI and tensor components is less than 3 nT and 20 nT/m, respectively, and the estimation of the parameters is highly robust.