Abstract:

In order to determine aircraft performance stability and contrdl characteristics from test data, flight stale estimation is needed.Usually, extended, Kalman filter and fixed-interval smoother are used for handling the nonlinear estimation problems of both the state and parameter.However, practical experience and theoretical studies show that this augmented algorithm, in fact, is not satisfactory for parameter estimation.In order to provide a more accurate and practical method for flight state estimation, this paper presents a new separated bias identification and state estimation algorithm which is used for both the flight state estimation and instrumentation errors identification.In order to get high accuracy, U-D factorization methods are applied to improving the computational stability and efficiency. Discrete-time model of aircraft motion used for decreasing modeling errors.Finally, this new approach has been compared with the conventional method by digital simulation and actual flight test data compatibility check.The results calculated show that the new method can give more consistent results than the usual one for a different initial values and the noise statistics.Moreover, this approach can be used for the compatibility check of lower sampled flight test data.

Key words: nonlinear filter, bias identification, data processing, flight test