Kalman filter-based discrete data estimation for linear dynamic wireless channels

Channel estimation is an essential part in many detection techniques proposed for data transmission over dynamic channels. Kalman filter based channel estimation which is a widely adopted technique for channel tracking/estimation is adopted throughout this paper. In order to use Kalman filter estimator for the channel impulse response, we introduce a combined state-space model representation of the communication system. More precisely, the aim in this work is to present clearly and motivate rigorously the introduction of the Kalman filter in the estimation of Markov sequences through Gaussian dynamical channels. In this way, we interpret and make clearer the underlying approximations in the heuristic approaches used throughout this paper. Simulation results are provided to analyze the performance of proposed algorithm compared to the conventional fixed step size PSP (per survivor processing).