A SVD-Based Extended Kalman Filter and Application to Flight State and Parameter Estimation of Aircraft
Youmin M. Zhang · Control theory & applications · 1996
In this paper,a new robust extended Kalman filtering algorithm based on singular value decomposition (SVD) of covariance information matrix is presented with application tothe flight state and parameter estimation of aircraft. The presented algorithm not only has agood numerical stability but also can handle correlated measurement noise without any additional transformation. The algorithm is formulated in the form of vector-matrix operations,so it is also useful for parallel computers. The applications to the flight state and parameter-estimationby simulated and actual flight test data computation of two types of Chinese aircraft show thatthe new algorithm presented in this paper can give more accurate estimates of flight state andparameter than extended Kalman filter (EKF) for different initial values and noise statistics.Moreover, the new algorithm has less requirements for the maneuvering shapes,noise levels,data length and better convergency than those of EKF. The computational requirements of thenew filtering algorithm have been reduced greatly by exploiting some special features of matrixcomputation and system model. It is proved that the new filtering algorithm can give good results even for low sample rate flight test data.