Adaptive spectral factorization

An on-line spectral factorization algorithm is used to devise a globally convergent self-tuning identifier that does not suffer from restrictions that amount to knowledge of the true system (e.g. the positive real condition). The method developed uses two ideas. One idea, an old one which might be called the method of split recursions, is used to estimate the parameters in blocks. Thus, one block might get the transfer function parameters while the other gets the noise parameters. The other idea is to use spectral factorization to estimate moving average parameters. The algorithm does have its own weaknesses (e.g. transient behavior may not be good, and it relies on a condition that is only generically true), but it does not need a positive real condition to be satisfied for global convergence