Underdetermined Blind Mixing Matrix Estimation Based on Normal Vector of Hyperplane

When sources are not strictly sparse,the algorithms of underdetermined blind mixing matrix estimation based on the sparsity of sources usually firstly cluster the hyperplanes generated by the mixing vector,and then estimate the mixing matrix.However,this method requires the calculation of hyperplane clustering whose computa-tion load is heavy and efficiency is low.To address this issue,a new algorithm is proposed.First,the normal vector of hyperplane is calculated by the proposed normal vector renew formula based on the gradient method,and then the mixing matrix is estimated.In this way,hyperplane clustering is avoided.The proposed algorithm has lower computational cost and the efficiency of the estimation of mixing matrix is well improved.The simulation results verify the accuracy and the effectiveness of the proposed algorithm.