KALP: a Kalman filter-based adaptive clock method with low-pass prefiltering for packet networks use

We consider the issue of source clock frequency recovery in packet networks and propose a new adaptive clock method based on the Kalman filter (KF) with low-pass prefiltering-KALP (Kalman filter-based Adaptive clock method with Low-pass Prefiltering) in short. Noting that because of the difficulty in modeling as well as the nonwhite Gaussian nature of the packet jitter most existing adaptive clock methods could not successfully adopt the Kalman filter, we take a new approach to packet jitter modeling for the KALP. We model the packet jitter not directly but after shaping its characteristics by low-pass prefiltering. This low-pass prefiltering is an important arrangement as it helps to convert the packet jitter into a low-pass signal regardless of its original characteristics, thus enabling to model the prefiltered packet jitter using a simple first-order autoregressive [AR(1)] process. The low-pass prefilter should be carefully selected not to lose the timing information while prefiltering, and the moving averager employed in this paper satisfies this requirement. The AR(1)-modeled jitter component is amenable to the KF-based processing, which in this case becomes an optimal estimator. The design parameters including the initial conditions of the KF and AR(1) parameters can be determined based on the service clock specification and packet interarrival times during the delay smoothing process. We carry out various simulations to compare the performance of the KALP with the existing buffer-based adaptive clock method and demonstrate that the KALP can significantly reduce the fluctuation in the level of receiving buffer as well as the time to recover the source clock frequency