摘要
There are two major approaches for Blind Signal Separation (BSS) problem: Maximum Entropy (ME) and Minimum Mutual Information (MMI) algorithms. Based on the recursive architecture and the relationship between the ME and MMI algorithms, an Extended ME(EME) algorithm is proposed by using probability density function (pdf) estimation of the outputs to deduce the corresponding iterative formulas in BSS. Based on the simulation results, it can be concluded that the proposed algorithm has better performances than the traditional ME algorithm in convolute mixture BSS problems.
There are two major approaches for Blind Signal Separation (BSS) problem: Maximum Entropy (ME) and Minimum Mutual Information (MMI) algorithms. Based on the recursive architecture and the relationship between the ME and MMI algorithms, an Extended ME(EME) algorithm is proposed by using probability density function (pdf) estimation of the outputs to deduce the corresponding iterative formulas in BSS. Based on the simulation results, it can be concluded that the proposed algorithm has better performances than the traditional ME algorithm in convolute mixture BSS problems.
