Developing Adaptive Noise Reduction Techniques for Improved Kalman Filter Performance in Noisy BitTorrent Traffic Data.pdf

The Kalman filter is a widely used technique for estimating system states in the presence of noise; however, its performance can degrade in highly dynamic and noisy environments such as BitTorrent traffic data. This study proposes adaptive noise reduction techniques to enhance Kalman filter performance in processing noisy peer-to-peer (P2P) network data. By integrating machine learning-based noise modeling, wavelet transform-based denoising, and adaptive covariance estimation, the proposed approach dynamically adjusts to variations in traffic noise characteristics. The research evaluates the effectiveness of these techniques using real-world BitTorrent traffic datasets, comparing them against traditional Kalman filtering methods. Experimental results demonstrate significant improvements in state estimation accuracy, noise resilience, and computational efficiency. These findings highlight the potential of adaptive noise reduction methods in optimizing Kalman filter applications for robust P2P network analysis and traffic monitoring.