Cubature Kalman Filter (CKF) offers a promising solution to handle the data fusion of integrated nonlinear INS/GNSS (Inertial Navigation System/Global Navigation Satellite System) navigation. However, its accuracy is degraded by inaccurate kinematic noise statistics which originate from disturbances of system dynamics. This paper develops a method of closed-loop feedback covariance control to address the above problem of CKF. In this method, the posterior state and its covariance are fed back to the filtering process to constitute a closed-loop structure for CKF covariance propagation. Subsequently, based on the maximum likelihood principle, a control scheme of the prior state covariance is established by using the feedback state and covariance within an estimation window and further adopting a proportional coefficient to amplify the feedback terms in recent time steps for the full use of new information to reflect actual system characteristics. Since it does not directly use kinematic noise covariance, the proposed method can effectively avoid the adverse impact of inaccurate kinematic noise statistics on filtering solutions. Further, it can also guarantee the prior state covariance to be positive semi-definite without involving extra measures. The efficacy of the proposed method is validated by simulations and experiments for integrated INS/GNSS navigation.
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