Abstract: Underdetermined blind source separation is studied in this paper.In order to make use of time-frequency characteristic of signal，a new method of variational estimation on signal frequency by using AR model power spectrum estimation，designing band-pass filter to gain approximatively source signals and underdetermined mixed matrix，and constructing completed observation signals by expanded subspace primer vector is proposed.Thus original blind source separation problem is transformed from underdetermined to completed situation.Finally blind source separation is achieved by FastICA method.The simulation experiment results show that the proposed method is feasible and effective，which provides new way for study on underdetermined blind source sepatation.

Key words: underdetermined blind source separation, AR model, power spectrum estimation, expanded subspace, FastICA

摘要： 研究了欠定情形下的信号盲分离。充分利用信号的时频特性，提出了AR模型功率谱估计法滑动估计信号频率，设计带通滤波器近似获取源信号和欠定混合矩阵，以及扩展子空间向量基构造完备观测信号的方法，将问题转化为完备情况下的盲分离，最后运用FastICA方法实现了信号盲分离。仿真实验数据表明方法的可行性和有效性，为欠定盲分离问题研究提供了新的思路。

关键词: 欠定盲分离, AR模型, 功率谱估计, 扩展子空间, FastICA