Abstract: new nonlinear approximate conditional mean filter is presented and applied to the flight state and parameter estimation of aircraft.The results of simulated and actual flight test data computations show that the new filter proposed here can give much more accurate estimates of flight state than extended Kalman filter(EKF)for different initial values and noise statistics.Moreover,the new method has fewer requirements for the maneuvering shapes,noise levels,data length and better convergency than that of EKF. The computational and storage requirements for one-step filtering updates of the new filter have been reduced greatly by exploiting some special features of system and measurement models and this makes it possible for the nonlinear filtering algorithm to be on-line realized on IBM-personal computer.It is proved that the new filter will be suitable for state and parameter of flight region at high angles of attack and can give good re-sults even for low sample rate flight test data.

Key words: nonlinear filters-approximation, Kalman filters, flight conditions-parameter identifi-cation, flight tests