Estimating 3D Signals with Kalman Filter

TL;DR

This paper implements an optimized Kalman filter to denoise 3D signals affected by Gaussian noise, utilizing fast algorithms for noise measurement and state prediction to improve performance and efficiency.

Contribution

It introduces a fast, parameter-adjusted Kalman filter framework for effective 3D signal denoising with reduced computational complexity.

Findings

Enhanced noise reduction in 3D signals

Reduced computational load compared to standard methods

Maintained signal integrity after filtering

Abstract

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