An Intelligent Ellipsoid Calibration Method Based on the Grey Wolf Algorithm for Magnetic Compass

With the measurement of the Earth’s magnetic field,magnetic compass can provide high frequency heading information.However,it suffers from local magnetic interference.An intelligent ellipsoid calibration method based on the grey wolf is proposed to generate optimal parameters for magnetic compass to generate high performance heading information.With the analysis of the projection relationship among the navigation coordinate frame,the body frame and the local horizontal frame,the heading ellipsoid equation is constructed.Furthermore,an improved grey wolf algorithm is proposed to find optimization solution in a large solution space.With the improvement of the convergence factor and the evolutionary mechanism,the improved grey wolf algorithm can generate optimized solution for heading ellipsoid equation.The effectiveness of the proposed method has been verified by a series of vehicle and flight tests.The experimental results show that the proposed method can eliminate errors caused by sensor defects,hard-iron interference,and soft-iron interference effectively.The heading error generated by the magnetic compass is less than 0.2162 degree in real flight tests.

Multisensor fusion for an experimental airship based on strong tracking filter

To improve the independent ability of attitude determination and positioning for an unmanned experimental airship platform, a micro inertial measurement system (MIMS) is expected to integrate with the existing system, which incorporates a digital magnetic compass and a differential pseudorange GPS receiver. The navigation error of the low-precision MIMS will be calibrated using nondrift DGPS receiver and magnetic compass. This paper proposes an adaptive strong tracking filter to perform multisensor fusion to assure state-error estimation of convergence under some uncertain conditions. These uncertainties include model simplification, unknown microsensor stochastic characteristics, a large-scale initial filtering parameter variation, and state sudden change. Monte Carlo simulations demonstrate the filter has strong robustness to all the uncertainties mentioned above. By this filtering approach, the navigation errors of MIMS are limited to a certain range. Accordingly, the whole integrated measurement system will respond to dynamics, and its automotive navigation ability is also enhanced.