We propose a novel source recovery algorithm for underdetermined blind source separation, which can result in better accuracy and lower computational cost. On the basis of the model of underdetermined blind source sep...We propose a novel source recovery algorithm for underdetermined blind source separation, which can result in better accuracy and lower computational cost. On the basis of the model of underdetermined blind source separation, the artificial neural network with single-layer perceptron is introduced into the proposed algorithm. Source signals are regarded as the weight vector of single-layer perceptron, and approximate ι~0-norm is taken into account for output error decision rule of the perceptron, which leads to the sparse recovery. Then the procedure of source recovery is adjusting the weight vector of the perceptron. What's more, the optimal learning factor is calculated and a descent sequence of smoothed parameter is used during iteration, which improves the performance and significantly decreases computational complexity of the proposed algorithm. The simulation results reveal that the algorithm proposed can recover the source signal with high precision, while it requires lower computational cost.展开更多
针对欠定盲分离中时变混合矩阵的估计问题,在稀疏域二维最小偏差角算法的基础上,提出了一种改进的欠定盲分离时变混合矩阵估计算法。该算法通过判断原始阵各列上是否都有观测点聚集和聚集在原始阵上的观测点以外的点的聚集方向,来检测...针对欠定盲分离中时变混合矩阵的估计问题,在稀疏域二维最小偏差角算法的基础上,提出了一种改进的欠定盲分离时变混合矩阵估计算法。该算法通过判断原始阵各列上是否都有观测点聚集和聚集在原始阵上的观测点以外的点的聚集方向,来检测变化时刻;并利用基于点密度大区域检测算法估计混合矩阵。改进算法对于混合矩阵发生某些列增加、消失和变化时均能检测出变化,并且在大幅提高变化时刻检测概率和混合矩阵估计精度的同时,降低了复杂度。实验仿真结果表明,在20 d B信噪比时,混合矩阵估计精度提高了60%以上。展开更多
基金supported by National Nature Science Foundation of China under Grant (61201134, 61401334)Key Research and Development Program of Shaanxi (Contract No. 2017KW-004, 2017ZDXM-GY-022)
文摘We propose a novel source recovery algorithm for underdetermined blind source separation, which can result in better accuracy and lower computational cost. On the basis of the model of underdetermined blind source separation, the artificial neural network with single-layer perceptron is introduced into the proposed algorithm. Source signals are regarded as the weight vector of single-layer perceptron, and approximate ι~0-norm is taken into account for output error decision rule of the perceptron, which leads to the sparse recovery. Then the procedure of source recovery is adjusting the weight vector of the perceptron. What's more, the optimal learning factor is calculated and a descent sequence of smoothed parameter is used during iteration, which improves the performance and significantly decreases computational complexity of the proposed algorithm. The simulation results reveal that the algorithm proposed can recover the source signal with high precision, while it requires lower computational cost.
文摘针对欠定盲分离中时变混合矩阵的估计问题,在稀疏域二维最小偏差角算法的基础上,提出了一种改进的欠定盲分离时变混合矩阵估计算法。该算法通过判断原始阵各列上是否都有观测点聚集和聚集在原始阵上的观测点以外的点的聚集方向,来检测变化时刻;并利用基于点密度大区域检测算法估计混合矩阵。改进算法对于混合矩阵发生某些列增加、消失和变化时均能检测出变化,并且在大幅提高变化时刻检测概率和混合矩阵估计精度的同时,降低了复杂度。实验仿真结果表明,在20 d B信噪比时,混合矩阵估计精度提高了60%以上。
