基于UCA压缩感知的声源定位算法

Sound source localization algorithm based on UCA compressed sensing

针对混合声源定位精度低的问题,提出了一种非迭代补全互谱矩阵的算法,利用互谱矩阵的埃尔米特特性,不经过迭代,在不牺牲定位精度的情况下提高计算效率。该方法首先建立一个稀疏模型,通过构造冗余脉冲响应(RIR)矩阵作为压缩感知测量矩阵,将源定位问题转化为压缩感知问题。然后根据多个源方向向量的空间稀疏相关性,引入投影算子,在压缩感知框架下使方位角的均方根误差保持在5%以内。均匀圆阵(UCA)环境下的仿真结果表明,与传统算法相比,该算法具有更好的估计性能。

Aiming at the problem of low positioning accuracy of mixed sound source,a non iterative algorithm to complete cross spectral matrix is proposed,which uses the Hermitian characteristics of the cross-spectrum matrix without iteration and improves the computational efficiency without sacrificing positioning accuracy.This method first establishes a sparse model,and converts the source localization problem into a compressed sensing problem by constructing a redundant impulse response(RIR)matrix as a compressed sensing measurement matrix.Then,according to the spatial sparse correlation of multiple source direction vectors,a projection operator is introduced,and the root mean square error of azimuth is kept within 5%under the framework of compressed sensing.The simulation results in the uniform circular array(UCA)environment show that the algorithm has better estimation performance than the traditional algorithm.