Research And Applications Of BSS Algorithm Based On Fast ICA

Blind Sources Separation (BSS) is one of the multiple signals processing method; it is the process of recovering unknown independent source signals from sensor measurements which are unknown combinations of the source signals. BSS has become a hot topic of modern signal processing, so it has very important theory significance and utility value in communication, speech processing, image processing, biomedicine and radar technology, and it even can be applied to financial data analysis.This paper studies an important blind source separation algorithm-Fast Independent Component Analysis (Fast ICA) and its applications in seismic data denoising and noisy bind image separation.BSS theories are firstly presented, and then Independent Component Analysis (ICA) is derived from BSS.Firstly, the mathematical model and principle of ICA are studied, and the different independent criteria and several main algorithms of ICA are discussed. Further, the Fast ICA algorithm is studied. Fast ICA is a fast algorithm of ICA, which is based on Fixed-point iteration theory to fix the non-Gaussian maximum. Fast ICA algorithm parallelly processes a large amount of sample point of received signals via Newton iterative algorithm, and recovers one independent component from the receiving signals one time.Secondly, the Fast ICA algorithm in wavelet domain is studied, and is applied in bind image separation. The experiment results prove the advantages of wavelet domain based Fast ICA algorithm.Finally, the application of the Fast ICA algorithm is studied. First, the Fast ICA algorithm is used to removal the random noise of seismic data, simulated and real noisy seismic data are used to simulation experiment. The results prove the effective of this algorithm in seismic processing. Second, the Fast ICA algorithm and Curvelet transform are combined to separate noisy bind image. The simulation experiment shows the superiority of this algorithm.