A new bias partitioned square-root information filter and smoother for aircraft flight state and parameter estimation

Abstract

A novel bias partitioned square-root information filter (PSRIF) with an associated partitioned square-root information smoother (PSRIS) for aircraft flight state and parameter estimation is proposed. This algorithm not only can improve the numerical robustness and precision of flight state estimation but can also make the computation more efficient than the augmented extended Kalman filter or the conventional square-root information filter and square-root information smoother (SRIF/SRIS). Results of simulated and actual flight test data computation on two types of Chinese aircraft show that the proposed method can give accurate estimates of flight state and parameter for high and low sampling rates and is much more numerically stable and efficient that the other techniques considered. >