State estimation in Rice/Rayleigh channels using a nonlinear filtering technique

The paper develops a stochastic algorithm for nonlinear state estimation of a Rice/Rayleigh flat-fading channel. The authors adopt a nonlinear filtering (Bayesian) approach whereby the conditional probability densities are represented as a product of a Gaussian function (fading information) and a Tikhonov function (phase drift/Doppler shift information). The resulting algorithm, suitable for carrier recovery in open-loop digital receivers, is hangup-free, has low complexity and shows improved performance with respect to the extended Kalman-Bucy filter. Significant frequency offsets and fast fading channel conditions can be tracked if sub-symbol sampling is used