Practical considerations in the use of a new OBE algorithm that blindly estimates error bounds

Optimal bounding ellipsoid (OBE) identification algorithms require precise knowledge of bounds on model disturbance sequences, and such bounds are often difficult to ascertain in practice. The OBE algorithm with automatic bound estimation (OBE-ABE) theoretically obviates the need for precise a priori bound estimates, thereby removing the major obstacle to practical application of these powerful and interesting methods. Performance assessment of OBE-ABE, particularly with regard to its favorable convergence behavior, has involved asymptotic analysis over infinite frames of data. This paper discusses application of OBE-ABE to short-time frames of data, suggesting that the favorable asymptotic results apply to finite processing if care is taken in the choice of certain parameters. Example case studies, one using real speech data, illustrate the theoretical discussions.