The amplitude-locked loop separation system using Kalman fuzzy algorithm [Kalman filter]

In this paper, we present a Kalman filter algorithm with fuzzy theory, which shows how to search for the optimum weight coefficient values of a Kalman filter. In the traditional Kalman filter algorithm, we discover some drawbacks such as low convergence time and large mean square error (MSE). Therefore, we adopt a Kalman algorithm combined with fuzzy logic theory for improving the convergence rate and the MSE value of the conventional Kalman filter using simulated results under an additive white gaussian noise (AWGN) channel. The major goal of this paper is to show that a Kalman filter, which is based on fuzzy logic for separating the signals under an AWGN channel, has better performance than the conventional Kalman algorithm.