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In practical applications, an electronic compass composed of a three-axis magnetometer and a three-axis accelerometer is often due to the strong magnetic field interference of the carrier, which makes the magnetometer unable to accurately calculate the heading, and even causes the electronic compass to fail. This problem puts forward more stringent requirements on the installation and use of the electronic compass on the carrier. Generally, traditional magnetic field compensation methods such as ellipse/ellipsoid fitting method can effectively compensate electronic compasses. However, these methods cannot be used when the output of the magnetometer is saturated. In order to overcome the above influence, this paper proposes a three-axis external coil active compensation method. By analyzing the error characteristics of the magnetometer, a coil compensation model was established, and a neural network-based control algorithm was designed to realize the adaptive compensation of current. The use of genetic algorithm can improve the global search ability, while controlling the convergence of the BP neural network, which has better real-time performance and higher reliability. The simulation experiment results show that the method has better optimization ability and higher compensation accuracy, the strong magnetic interference of the carrier is effectively compensated, and the working environment adaptability of the MEMS electronic compass is significantly improved.

Active Compensation Method for Strong Magnetic Interference of MEMS Electronic Compass