Abstract
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.
Highlights
With the rapid development of micro electromechanical system (MEMS) technology, developing accurate long-term positioning systems based on the MEMS inertial devices without any external signals may become possible
The hard iron disturbances are caused by ferromagnetic materials with a permanent magnetic field, e.g., magnets and speakers, which are time-invariant, whereas the soft iron disturbances are deflections or alterations in the existing magnetic field, which are caused by magnetized materials such as steel shield and batteries [2]
LS/maximum likelihood (ML) was almost equal to recursive least square (RLS)/ML in the accuracy, RLS/ML could effectively shorten the computational time and save memory space, which is beneficial to the engineering application
Summary
With the rapid development of micro electromechanical system (MEMS) technology, developing accurate long-term positioning systems based on the MEMS inertial devices without any external signals may become possible. Due to the weak yaw observation of the conventional navigation systems, magnetometers are widely utilized for heading estimation based on the principle that the local magnetic field points to the north [1,2,3,4]. The magnetic disturbances can be classified into hard iron and soft iron disturbances. The hard iron disturbances are caused by ferromagnetic materials with a permanent magnetic field, e.g., magnets and speakers, which are time-invariant, whereas the soft iron disturbances are deflections or alterations in the existing magnetic field, which are caused by magnetized materials such as steel shield and batteries [2]
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