  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.
