  In this paper, the standard Kalman filter was implemented to denoise the
three dimensional signals affected by additive white Gaussian noise (AWGN), we
used fast algorithm based on Laplacian operator to measure the noise variance
and a fast median filter to predict the state variable. The Kalman algorithm is
modeled by adjusting its parameters for better performance in both filtering
and in reducing the computational load while conserving the information
contained in the signal
