  The frequency-domain Kalman filter (FKF) has been utilized in many audio
signal processing applications due to its fast convergence speed and
robustness. However, the performance of the FKF in under-modeling situations
has not been investigated. This paper presents an analysis of the steady-state
behavior of the commonly used diagonalized FKF and reveals that it suffers from
a biased solution in under-modeling scenarios. Two efficient improvements of
the FKF are proposed, both having the benefits of the guaranteed optimal
steady-state behavior at the cost of a very limited increase of the
computational burden. The convergence behavior of the proposed algorithms is
also compared analytically. Computer simulations are conducted to validate the
improved performance of the proposed methods.
