Kalman filter based system identification exploiting the decorrelation effects of linear prediction

Abstract

In system identification one problem is the autocorrelation of the excitation signal which often crucially affects the adaptation process. This paper focuses on the Kalman filter based adaptation working in the frequency domain and the implication due to correlated signal input. Principle simulations and the introduction of a reference model indicate to which extent correlation take effect. The experimental results demonstrate that even though the Kalman approach already takes advantage from a certain level of inherent decorrelation, it also benefits from additional decorrelation. To address this issue, we derive a new realizable efficient structure combining the Kalman filter based adaptation with linear prediction techniques. The performance gains of the proposed approach are confirmed via experiments for an acoustic echo cancellation application for different scenarios.