Computational Improvement of the Fast H∞ Filter Based on Information of Input Predictor

A computationally reduced version of the fast H_infin filter (FHF) is derived under the assumption that the input signal to the unknown system can be represented by an autoregressive (AR) model whose order M is much lower than the filter length N. The resulting filter, referred to as the predictor-based fast H_infin filter (P-FHF), has a computational requirement of 3N+O(M) multiplications per iteration, which is considerably lower than the requirement for the FHF if is sufficiently smaller than N . The validity of the P-FHF are confirmed by computer simulations.