For Inertial Navigation System(INS)/Celestial Navigation System(CNS)/Global Navigation Satellite System(GNSS)integrated navigation system of the missile,the performance of data fusion algorithms based on the Cubature ...
详细信息
For Inertial Navigation System(INS)/Celestial Navigation System(CNS)/Global Navigation Satellite System(GNSS)integrated navigation system of the missile,the performance of data fusion algorithms based on the Cubature Kalman Filter(CKF)is seriously degraded when there are non-Gaussian noise and process-modeling errors in the system ***,a novel method is proposed,which is called Optimal Data Fusion algorithm based on the Adaptive Fading maximum Correntropy generalized high-degree CKF(AFCCKF-ODF).First,the Adaptive Fading maximum Correntropy generalized high-degree CKF(AFCCKF)is proposed and used as the local filter for the INS/GNSS and INS/CNS subsystems to improve the robustness of local state ***,the local state estimation is fused based on the minimum variance principle and highdegree cubature criterion to get the globally optimal ***,the experimental results verify that the proposed algorithm can significantly improve the robustness of the missile-borne INS/CNS/GNSS integrated navigation system to non-Gaussian noise and processmodelingerror and obtain the global optimal navigation information.
This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the b...
详细信息
This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error;at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.
暂无评论