Improved MEMS Magnetometer Adaptive Filter Noise Reduction and Compensation Method
Zhiwen Ning, Jun Fu, Yang Chang, Teng Lyu
Abstract
In the actual integrated navigation process of the MEMS three-axis magnetometer, excessive measurement noise and external magnetic field interference often cause the magnetometer heading to be inaccurate, thereby reducing the overall performance of the integrated navigation system. In order to overcome the above influence, this paper proposes an adaptive real-time filtering noise reduction and comprehensive error interference compensation method. We have completed the construction of the measurement noise model and parameter update by analyzing the measurement noise characteristics of the MEMS three-axis magnetometer. We designed an adaptive filtering algorithm with multiple fading memory factors to perform offline noise reduction on the output data of the magnetometer, and use the noise-reduced data for comprehensive compensation of magnetic field interference, which has a good effect. The experimental results show that the proposed method has better filtering and noise reduction ability and higher compensation accuracy in offline state, can effectively reduce noise and compensate the magnetometer, and improve the accuracy of the magnetometer’s heading calculation.