一种最大密度检测欠定混合矩阵估计算法

针对源数未知条件下欠定盲源分离混合矩阵估计问题,提出了最大密度检测混合矩阵估计算法。在观测信号稀疏表示的基础上,首先对观测信号进行预处理;然后寻找观测信号的最大密度点;接着在此基础上确定有效样本点集合,再聚类得到辐射源数和混合矩阵。为验证算法的有效性,在时频单源点检测法和小波变换法下开展了仿真实验。结果表明,所提出算法的源数和混合矩阵估计效果优于参考算法,计算复杂度远低于参考算法。进一步实验表明,所提出算法对于正定、超定和欠定盲源分离混合矩阵的估计都具有较好的适用性。

Estimation algorithm for an underdetermined mixing matrix based on maximum density point searching

Aiming at mixing matrix estimation when the source number is unknown for underdetermined blind source separation (UBSS), a mixing matrix estimation method based on maximum density point searching is proposed. Based on sparse representation of observed signals, for the proposed algorithm, preprocessing of observed signals is processed first, and then the maximum density point of each observed signal is searched, after which the effective sample points are assembled, and then the source number and mixing matrix are estimated by the clustering method. For validation of the proposed algorithm, the simulations are developed by employing two sparse representation methods, which are single source point detection in the time-frequency domain and wavelet transform. Results show that the source number and the mixing matrix effect of the proposed algorithm are better than those of the reference algorithm, and that the calculation complexity of the proposed algorithm is much less than that of the reference algorithm. Further tests show that the proposed algorithm is applicable for mixing matrix estimation of positive-determined, overdetermined and underdetermined blind source separation models.