This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time...This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.展开更多
为了克服卷积混合信号盲源分离双最小均方(Double least mean squeres,Double-LMS)算法在分离滤波器过长时计算量过大的问题,借助于傅里叶变换理论中的帕斯维尔定理,将其转化为频域积分算法。频域积分算法可以利用快速傅里叶变换实现,...为了克服卷积混合信号盲源分离双最小均方(Double least mean squeres,Double-LMS)算法在分离滤波器过长时计算量过大的问题,借助于傅里叶变换理论中的帕斯维尔定理,将其转化为频域积分算法。频域积分算法可以利用快速傅里叶变换实现,具有较高的计算效率,可以克服当分离滤波器过长时原算法效率低下的问题。仿真结果表明:新算法在保持了DoubleLMS算法良好分离性能的基础上,降低了原算法的复杂度,提高了计算效率。展开更多
基金supported by the National Natural Science Foundation of China(61072120)
文摘This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.
文摘为了克服卷积混合信号盲源分离双最小均方(Double least mean squeres,Double-LMS)算法在分离滤波器过长时计算量过大的问题,借助于傅里叶变换理论中的帕斯维尔定理,将其转化为频域积分算法。频域积分算法可以利用快速傅里叶变换实现,具有较高的计算效率,可以克服当分离滤波器过长时原算法效率低下的问题。仿真结果表明:新算法在保持了DoubleLMS算法良好分离性能的基础上,降低了原算法的复杂度,提高了计算效率。
