A singularity eliminating algorithm based on Kalman filter for high altitude airship's SINS

To eliminate singularities which disturb the solution to high altitude airship's attitude, an algorithm based on the Kalman filter was proposed for excluding singularities. Based on the developed Kalman filtering model, the current state variables as priori estimates were substituted into the time-updating equations of the filter in order to be mapped onto the measurement-updating equations in time and get the data required in the measurement-updating equations. And then, the priori estimates were corrected by the measurement-updating equations so as to obtain the posterior estimates. The obtained posterior estimates were further substituted for the singularities from accelerometer sensors so as to eliminate the singularities. The Matlab experiments verify the effectiveness of the proposed algorithm, and the experimental results show that the algorithm eliminates signal burrs from the system's sensors effectively, with no delay introduced.