| Wen Jiangtao (温江涛),Zhao Qianyun,Sun Jiedi.[J].高技术通讯(英文),2016,22(1):82~89 |
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| Mixing matrix estimation of underdetermined blind source separation based on the linear aggregation characteristic of observation signals |
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| DOI：10.3772/j.issn.1006-6748.2016.01.012 |
| 中文关键词: |
| 英文关键词: underdetermined blind source separation (UBSS), sparse component analysis (SCA), mixing matrix estimation, generalized Gaussian distribution (GGD), linear aggregation |
| 基金项目: |
| Author Name | Affiliation | | Wen Jiangtao (温江涛) | | | Zhao Qianyun | | | Sun Jiedi | |
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| 中文摘要: |
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| 英文摘要: |
| Under the underdetermined blind sources separation (UBSS) circumstance, it is difficult to estimate the mixing matrix with high-precision because of unknown sparsity of signals. The mixing matrix 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’ correlation features affect the observation signals sparsity and further affected the directionality of time-frequency scatter plot. So a new mixing matrix estimation method is proposed for different sparsity degrees, 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 improved K-means clustering is applied to achieve the estimation of mixing matrix. The proposed algorithm 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 algorithm. |
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