基于估计参数势函数法的欠定盲分离

Underdetermined blind separation based on potential function with estimated parameter's decreasing sequence

针对k维子空间法尺度参数选择的盲目性,提出了基于估计参数的势函数方法。首先根据观测信号估计出基准值r1,并根据基准值r1选取势函数f的尺度参数σ1,由势函数f估计出聚类平面后,根据聚类平面估计出基准值r2,根据基准值r2选取势函数g的尺度参数σ2,并由势函数g估计出混合矩阵。该方法充分利用了观测信号的统计特性,实验结果表明,在无噪条件下,该方法比改进前的方法在矩阵估计方面误差减小了75%,较其他方法误差减小了1~2个数量级;在16dB信噪比下,该方法比改进前在矩阵估计精度上提高8.7%,源信号个数估计准确率提高了1倍。

A potential function method with estimated parameter is proposed for blindness of scale parameter selecting of k dimensional subspace method. Firstly, the reference value r1 is estimated based on observed signals and the scale parameter σ1 for potential function f is estimated based on r1. When cluster planes are estimated by f, the reference value r2 is estimated based on them and the scale parameter σ2 for potential function g is estimated based on r2. Thus, we obtain the estimation of mixing matrix. This method makes full use of the statistical properties of observed signals. The experiment shows that this method is reduced by 75 % in matrix estimation error than the method before improved and is reduced by 1 - 2 orders of magnitude compared with other methods when there is no noise; the estimated precision is 8. 7G higher and the accuracy rate of the source＇s number estimation is twice higher.