Fast and efficient delay estimation using local all-pass and kalman filters

Delay estimation is a common problem in signal processing which becomes particularly challenging when the delay is time-varying and the recorded signals are non-stationary. While methods for time-varying delay (TVD) estimation exist many of these are based on maximum likelihood estimation and thus are not well suited to real-time implementation. In this paper we present a method for TVD estimation which is suitable for real-time non-stationary applications. The proposed method combines local all-pass (LAP) filters with a Kalman filter. By using measurement fusion to combine the outputs of several LAP filters in the Kalman filter we can accurately track TVDs whilst allowing for fast and efficient parallel computation. Illustrative simulations demonstrate the effectiveness of the proposed approach.