Improved MEMS Magnetometer Adaptive Filter Noise Reduction and Compensation Method

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.

Automated summary

This paper proposes an adaptive real-time filtering noise reduction and comprehensive error interference compensation method to improve the performance of the integrated navigation system. By constructing the measurement noise model and updating parameters, using an adaptive filtering algorithm to perform offline noise reduction on the output data of the magnetometer, and compensating for magnetic field interference using the noise-reduced data, the experimental results show that this method has a good effect and can improve the accuracy of the magnetometer's heading calculation.