Estimating 3D Signals With Kalman Filter

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