School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China.
Sensors (Basel). 2020 Jan 18;20(2):535. doi: 10.3390/s20020535.
This study presents a new real-time calibration algorithm for three-axis magnetometers by combining the recursive least square (RLS) estimation and maximum likelihood (ML) estimation methods. Magnetometers are widely employed to determine the heading information by sensing the magnetic field of earth; however, they are vulnerable to ambient magnetic disturbances. This makes the calibration of a magnetometer inevitable before it is employed. In this paper, first, a complete measurement error model of the magnetometer is studied, and a simplified model is developed. Then, the real-time RLS algorithm is introduced and discussed in detail, and the unbiased optimal ML is utilized to improve the accuracy of the parameter estimation. The proposed algorithm is advantageous in correcting the parameters in real time and simultaneously obtaining unbiased parameter estimation. Finally, the simulation and experimental results demonstrate that both the accuracy and computational speed of the proposed algorithm is better than those of the widely used bath-processing method. Moreover, the proposed calibration method can be adopted for calibrating other three-axis sensors.
本研究提出了一种新的三轴磁强计实时标定算法，该算法结合了递推最小二乘法（RLS）估计和最大似然（ML）估计方法。磁强计广泛用于通过感测地球磁场来确定航向信息；然而，它们容易受到环境磁场干扰。因此，在使用磁强计之前，必须对其进行标定。在本文中，首先研究了磁强计的完整测量误差模型，并建立了简化模型。然后，详细介绍并讨论了实时 RLS 算法，并利用无偏最优 ML 来提高参数估计的准确性。所提出的算法在实时校正参数方面具有优势，同时可以获得无偏的参数估计。最后，仿真和实验结果表明，所提出算法的准确性和计算速度均优于广泛使用的整体处理方法。此外，所提出的标定方法可用于标定其他三轴传感器。
