This letter deals with the estimation of a flat fading Rayleigh channel with Jakes's spectrum. The channel is approximated by a first-order autoregressive (AR(1)) model and tracked by a Kalman Filter (KF). The common method used in the literature to estimate the parameter of the AR(1) model is based on a Correlation Matching (CM) criterion. However, for slow fading variations, another criterion based on the Minimization of the Asymptotic Variance (MAV) of the KF is more appropriate, as already observed in few works [1]. This letter gives analytic justification by providing approximated closed-form expressions of the estimation variance for the CM and MAV criteria, and of the optimal AR(1) parameter.

In this paper, we present a closed-form expression of a Bayesian Cramér-Rao lower bound for the estimation of a dynamical phase offset in a non-data-aided BPSK transmitting context. This kind of bound is derived considering two different scenarios: a first expression is obtained in an off-line context and then, a second expression in an on-line context logically follows. The SNR-asymptotic expressions of this bound drive us to introduce a new asymptotic bound, namely the Asymptotic Bayesian Cramér-Rao Bound. This bound is close to the classical Bayesian bound but is easier to evaluate.

Abstract-In this paper, the issue of audio source separation from a single channel is addressed, i.e. the estimation of several source signals from a single observation of their mixture. This challenging problem is tackled with a specific two levels coderdecoder configuration. At the coder, source signals are assumed to be available before the mix is processed. Each source signal is characterized by a set of parameters that provide additional information useful for separation. We propose an original method using a watermarking technique to imperceptibly embed this information about the source signals into the mix signal. At the decoder, the watermark is extracted from the mix signal to enable an end-user who has no access to the original sources to separate these signals from their mixture. Hence, we call this separation Index Terms-under-determined source separation, watermarking, audio processing, speech processing.