Kalman filtering algorithm for blind source separation

The paper presents a Kalman filtering algorithm based on nonlinear principal component analysis (PCA) with prewhitening for blind source separation (BSS), and compares the new algorithm with other algorithms. Simulations show that, for BSS, the Kalman filtering algorithm has a faster convergence rate and a much better tracking capability, compared with the existing natural gradient algorithm for independent component analysis (ICA), the RLS algorithm and the natural gradient based RLS-type algorithm for nonlinear PCA.