Total Least Squares Estimation of Dynamical Systems

The Total least squares error criterion is considered for estimation problems. Exact nec-essary conditions for the total error criterion have been derived. Several solution method-ologies are presented to solve the modified normal equations obtained from the necessary conditions. The results are applied on the parameter identification of a novel morphing wing developed at Texas A & M University (Static Problem). Subsequently, a filter is derived, with a multilinear measurement model, whose minimum variance estimator is shown to also minimize the total least squares type cost function. The filter thus derived is compared with a classical Kalman filter on a numerical example. I