Algorithms for aircraft parameter estimation accounting for process and measurement noise

Abstract

Synopsis: A comparison is made of four algorithms for parameter estimation in linear and nonlinear systems accounting for both process and measurement noise using two different approaches: direct approach and filtering approach. In the direct approach, the iterative Gauss-Newton method incorporating a suitable state estimator is used to estimate the unknown parameters by optimization of the likelihood function. For the state estimation both time-varying and steady-state filters are used. In the filtering approach, the unknown parameters are estimated as augmented states using the extended Kalman filter. The various algorithms are used to estimate from simulated as well as flight-test data the aircraft dimensional and nondimensional derivatives. Three model postulates, one linear and two nonlinear, are employed for this purpose. The parameter-estimation results indicate that the Gauss-Newton method with a steadystate filter, found to be adequate for the typical aircraft-estimation examples considered in the paper, is generally preferable. In the event that it becomes necessary to incorporate a time-varying filter, the filtering approach appears to be a viable alternative. Different aspects, such as convergence, computational time, parameter estimates, and their accuracies are evaluated for each of the four estimation algorithms. A general set of conclusions has been drawn.