Underdetermined Blind Source Separation for linear instantaneous mixing system in the non-cooperative wireless communication

Under the condition of non-cooperative wireless communication, many signals always overlap in time–frequencyfield, therefore, the signal separation and reconstruction of the received mixed signals is of great significance for the subsequent information processing. A new blind separation strategy is proposed to solve the blind separation problem in non-cooperative communication under general underdetermined conditions. Firstly, based on a new double-constrained single source points (SSP) detection criterion, a fuzzy mean clustering underdetermined blind identification (UBI) algorithm is proposed which got the high precision estimation of the mixing matrix. Then a singular value membership matching underdetermined source recovery (SVMMUSR) algorithm with dynamic k sparse component analysis ( k SCA) assumption is present. The singular value decomposition (SVD) method is applied to detect the membership of every sample data point with the subspace so as to obtain the optimal k -dimensional subspace matching with each data point. Subspace projection method is then used to achieve the accurate recovery of the signal for unknown k sparse conditions. Compared with other conventional methods, the simulation results indicate that the estimation performance and blind separation performance of the proposed method is better.