NASRUKF UAV based on combined navigation algorithm

Abstract

Combined navigation system is a navigation and positioning system composed of inertial navigation system and BeiDou satellite navigation system. Most of the navigation system models in combined navigation are nonlinear, but the traditional Kalman filtering algorithm is not well applied to nonlinear equations, and the Unscented Kalman filtering algorithm and Extended Kalman filtering algorithm which can be applied to nonlinear equations are constant in the fusion process of noise, so it will cause filtering divergence. In this paper, on the basis of Unscented Kalman filtering algorithm proposed will introduce the square root traceless Kalman filter algorithm, the algorithm through QR decomposition and Cholesk decomposition, the Sage-Husa algorithm combined with Square Root Unscented Kalman Filter algorithm, directly calculate the state error covariance matrix prediction and estimation of the square root factor, maintain the stability of the filtering, through practice proved that compared to Kalman filtering .The Nonlinear adaptive regression square root Kalman filter filter has a good navigation and positioning function, as the filtering is more convergent and the position accuracy can be within 5m, the speed error can be between 0.5m/s-1m/s. Compared with KF algorithm, the position error is increased by about 75%, and the speed error is increased by about 50%.