Mixing matrix estimation of underdetermined blind source separation based on the linear aggregation characteristic of observation signals

摘要

Under the underdetermined blind sources separation ( UBSS) circumstance, it is difficult to es-timate the mixing matrix with high-precision because of unknown sparsity of signals.The mixing ma-trix estimation is proposed based on linear aggregation degree of signal scatter plot without knowing sparsity, and the linear aggregation degree evaluation of observed signals is presented which obeys generalized Gaussian distribution ( GGD) .Both the GGD shape parameter and the signals’ correla-tion features affect the observation signals sparsity and further affected the directionality of time-fre-quency scatter plot.So a new mixing matrix estimation method is proposed for different sparsity de-grees, which especially focuses on unclear directionality of scatter plot and weak linear aggregation degree.Firstly, the direction of coefficient scatter plot by time-frequency transform is improved and then the single source coefficients in the case of weak linear clustering is processed finally the im-proved K-means clustering is applied to achieve the estimation of mixing matrix.The proposed algo-rithm reduces the requirements of signals sparsity and independence, and the mixing matrix can be estimated with high accuracy.The simulation results show the feasibility and effectiveness of the al-gorithm.