Non-Gaussian Sensor Fusion Analysis with “Gaussian Mixture and Adaptive” Based Cubature Kalman Filtering for Unmanned Aerial Vehicle

In this paper, comparison of adaptive and robust non Gaussian sensor fusion INS/GNSS is proposed to solve specific problem of non linear non Gaussian time variant state space estimation with impulsive measurement noise, different algorithms are proposed based on the most recent approaches in non linear filtering during intentional and non intentional interferences caused by multiple sources, or by the GNSS receivers signal degradation. Non linear filters such as Extended Kalman filter EKF and the last developed Cubature based Kalman Filter are implemented to estimate the navigation flight states for UAV. A specific hypothesis assumes high non Gaussian scenario and low non Gaussian noise level. Especially, alpha stable impulsive noise is simulated and affects GNSS measurement. 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 conventional non linear filters under non Gaussian constraints as a first step, thus, additional hypothesis is introduced which consists on Gaussian measurement outliers of each non Gaussian densities. In our work, Gaussian sum filtering and adaptive fading algorithms are proposed to solve the problem of IMU/GNSS integration in denied environment. 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 on the vari\ation 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. Two important results are carried out depending on the variation of the Gaussian mixture density modeled in this work. original results are observed, discussed with real perspectives in navigation data fusion for real time applications under multiple denied environment conditions.