Approved square root Cubature Kalman Filtering and its application to POS

To solve the problems that extended Kalman filter is difficult to obtain the optimal state estimation of complex nonlinear system with fast convergence speed and high estimate accuracy, an improved square root Cubature Kalman Filtering algorithm was proposed by introducing the matrix QR decomposition and Cholesky factorization updating technology to traditional Cubature Kalman Filter, via it can validly avoid the complicated calculating of matrix decomposition and inverse. Moreover, aiming at the uncertainty of system's variable and statistical properties, a weighted adaptive noise covariance matrix estimator was constructed, through integrating the adaptive noise estimator under wavelet Kalman Filtering ideology. A-SRCKF was applied to airborne positioning and orientation system, the simulation results demonstrate that the proposed method can effectively improve the accuracy of POS outputs as well as enhance the efficiency.