An adaptive cubature Kalman filter algorithm for inertial and land-based navigation system

Aimed at the problem of nonlinear and time-varying noise characteristics in inertial and land-based integrated navigation system, a cubature Kalman filter algorithm based on maximum a posterior estimation and fading factor has been proposed, and the fuzzy control theory is used to make it better to track the time-varying noise characteristics. Nonlinear measurement model of the land-based navigation system has been established. Online identification and adaptive adjustment of the measurement noise features has been realized by means of the designed noise estimator, which can effectively improve the estimation precision and inhibit filtering divergence. The simulation results show that the method proposed by the paper has a higher filtering accuracy compared with the traditional cubature Kalman filter. The horizontal positioning accuracy is improved by about 40%, and the horizontal velocity accuracy is improved by about 60%. The new algorithm can enhance the applicability of the land-based navigation system in required navigation performance.