Multiweight optimization in optimal bounding ellipsoid algorithms

Optimal Bounding Ellipsoid (OBE) algorithms offer an attractive alternative to traditional least-squares methods for identification and filtering problems involving affine-in-parameters signal and system models. The benefits-including low computational efficiency, superior tracking ability, and selective updating that permits processor multi-tasking-are enhanced by multiweight (MW) optimization in which the data history is considered in determining update times and optimal weights on the observations. MW optimization for OBE algorithms is introduced, and an example MW-OBE algorithm implementation is developed around the recent quasi-OBE algorithm. Optimality of the solution is discussed, and simulation studies are used to illustrate performance benefits.