基于时变自回归模型的FAJD盲源分离算法

为实现多高斯源和相关源信号的盲分离,在快速近似联合对角化(FAJD)算法的基础上,将故障诊断领域的时变自回归理论成功地应用于相关源信号的盲分离和多高斯源信号的盲分离.首先采用时变自回归模型(TVAR)对源信号建模,并通过白化预处理使得建模后的源信号具有可联合对角化的结构;然后,通过基函数加权和的方法将时变参数近似为已知基函数的加权和的形式,将其变成时不变的参数,再通过递推最小二乘法求解出模型系数矩阵组;最后,将所求出的系数矩阵组作为快速近似联合对角化的目标矩阵组,通过FAJD算法实现混合信号的分离.Matlab仿真实验验证了所提出的算法对于相关源信号和多高斯源信号的分离是行之有效的.由于算法中TVAR模型的优良特性,此算法非常适用于混合通信信号的盲分离.

FAJD blind source separation algorithm based on time-varying autoregressive model

In order to realize the blind separation of multi-Gaussian source and related source signals, based on the fast approximation joint diagonalization(FAJD) algorithm, the time-varying autoregressive theory in the field of fault diagnosis is successfully applied to the blind separation of correlated source signals and blind separation of multi-Gaussian source signals. Firstly, the time-varying autoregressive(TVAR) model is used to model the source signal. Then, the whitened pre-processing makes the modeled source signal have a structure that can be combined diagonally, and the time-varying parameters are approximated by the weighted sum of the basis functions. For the form of the weighted sum of the known basis functions, it becomes a time-invariant parameter, and then the model coefficient matrix group is solved using recursive least squares method, and it is used as the target matrix group of fast approximation and diagonalization. Finally, the separation of mixed signals is achieved using the FAJD algorithm. Matlab simulation experiments verify that the proposed algorithm is effective for the separation of correlated source signals and multi-Gaussian source signals. Due to the excellent characteristics of the TVAR model in the algorithm, this algorithm is very suitable for blind separation of mixed communication signals.