I. Introduction

The Maximum likelihood estimates of model parameters accounting only for measurement noise can be efficiently obtained for linear and general nonlinear systems. In Chapter 4, we discussed the output error method for this purpose in some detail, presenting the most commonly applied optimization algorithms and several practical issues. Although the output error method has been widely used in the past, and will continue to be used in the future as well, it is necessary to gather the data for estimation purposes from flight tests in a steady atmosphere. In the presence of atmospheric turbulence, the output error method yields poor estimation results, in terms of both convergence and estimates; we saw one such example in Chapter 4, Sec. XX.B.

There are two ways to account for turbulence in parameter estimation, 1) to measure the wind components, or more appropriately to derive them from other measured variables such as true airspeed, inertial speed, attitude angles, and flow angles, and 2) to model generically or explicitly the turbulence mathematically and estimate the corresponding parameters. The first approach is a data pre-processing step that yields wind components along the three bodyfixed coordinates, those can be treated as known inputs and accounted for in the estimation through minor modifications of the postulated models. The advantage is that the fairly simple output error method can be applied directly. The approach, however, requires precise measurements of the said variables. Any inaccuracies in the measurements, for example those resulting from calibration errors or time delays...