Turbo estimation algorithms: general principles, and applications to modalanalysis

In this paper, a new class of parameter estimation algorithms, called turbo estimation algorithms (TEA), is introduced. The basic idea is that each es timation algorithm (EA) must perform a sort of intrinsic denoising of the i nput data in order to achieve reliable estimates. Optimum algorithms implem ent the best possible noise reduction, compatible with the problem definiti on and the related lower bounds to the estimation error variance; however, their computational complexity is often overwhelming, so that in real life one must often resort to suboptimal algorithms; in this case, some amount o f noise could be still eliminated. The TEA methods reduce the residual nois e by means of a closed loop configuration, in which an external denoising s ystem, fed by the master estimator output, generates an enhanced signal to be input to the estimator for next iteration. The working principle of such schemes can be described in terms of a more general turbo principle, well known in an information theory context. In this paper, an example of turbo algorithm for modal analysis is described, which employs the Tufts and Kuma resan (TK) method as a master EA. (C) 2000 Elsevier Science B.V. All rights reserved.