Gaussian sum based adaptive cubature Kalman filtering applied to UAV's integrated navigation system

Abstract

In this paper, adaptive and robust non Gaussian sensor fusion INS/GNSS is proposed to solve specif-ic problem of non linear time variant state space estimation with measurement outliers, different algorithms are proposed to solve this specific problem generally occurs in intentional and non intentional interferences caused by other radio navigation sources, or by the GNSS receivers deterioration. Non linear approximation techniques such as Extended Kalman filter EKF and modern Cubature based Kalman Filters are computed to estimate the navigation states for UAV flight control. Several comparisons are conduced and analyzed in or-der to compare the accuracy and the convergence of different approaches usually applied in navigation data fusion purposes. The modern non linear filter algorithm called Cubature Kalman Filter CKF which provides more accurate estimation with more stability in Tracking data fusion application is compared with conven-tional non linear filters. In this work, CKF is compared with EKF in ideal conditions and during GNSS impulsive interferences modeled as non Gaussian noises “Sum of Gaussian” supposed to occur during specific interval of time, during the same interval, we assume additional denied environment which consists in the variation of the Gaussian sum noise covariance, then, innovation based adaptive fading approach is selected and used to modify the covariance calculation of the parallel non linear filters performed in this work. Interesting re-sults are observed, discussed with real perspectives in navigation data fusion for real time applications under multiple denied environment parameters.