Real-time Magnetometer Disturbance Estimation via Online Nonlinear Programming
For integrated navigation systems, this method improves magnetometer disturbance estimation under static motion, a challenging scenario where previous methods fail.
The paper tackles online estimation of unknown dynamic magnetic disturbances for magnetometer-based navigation. The proposed nonlinear programming method achieves high accuracy, fast response, and low computational load, outperforming prior methods in static motion bias estimation.
Magnetometer is a significant sensor for integrated navigation. However, it suffers from many kinds of unknown dynamic magnetic disturbances. We study the problem of online estimating such disturbances via a nonlinear optimization aided by intermediate quaternion estimation from inertial fusion. The proposed optimization is constrained by geographical distribution of magnetic field forming a constrained nonlinear programming. The uniqueness of the solution has been verified mathematically and we design an interior-point-based solver for efficient computation on embedded chips. It is claimed that the designed scheme mainly outperforms in dealing with the challenging bias estimation problem under static motion as previous representatives can hardly achieve. Experimental results demonstrate the effectiveness of the proposed scheme on high accuracy, fast response and low computational load.