Adaptive optimal bounded-ellipsoid identification with an error under-bounding safeguard: applications in state estimation and speech processing

Abstract

Optimal bounding ellipsoid (OBE) identification algorithms are noted for their simplicity and ability to leverage model error-bound knowledge for improved parameter convergence. However, the OBE convergence rate is dependent on the pointwise "tightness" of the model error-bound estimates. Since the least upper bound on the model error is often unknown, the convergence rate is compromised by the need to overestimate error-bounds lest the integrity of the process be violated by underestimation. We present an effective under-bounding safeguard against system model violations in OBE processing. Simulation examples in state estimation and speech processing demonstrate the efficacy of the under-bounding safeguard.