Simplex-directed partitioned adaptive filters

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Abstract
A new combination of mathematical programming methods and partitioned adaptive filters is proposed for the simultaneous real-time estimation of the parameters and state of an unknown linear system. The algorithm uses a version of the simplex method of non-linear programming to direct parameter selection for a bank of Kidman filters, thus combining the known convergence characteristics of partitioned adaptive filters with the robustness of this search technique. Motivation for the approach is discussed in light of recently published results concerning convergence of the decision function of the partitioned filters, and a simple example is given.

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