基于AVS和稀疏表示的鲁棒语者声源DOA估计方法

Robust Speaker DOA Estimation Based on AVS and Sparse Representation

基于声学矢量传感器（Acoustic vector sensor,AVS）和空间声源稀疏表示理论,进行了鲁棒的高精度语者声源到达角（Direction of arrival,DOA）估计方法研究。考虑混响和加性噪声影响,本文推导了AVS接收信号的向量化的协方差矩阵模型,设计了过完备字典,依此建立声源的空间稀疏表示模型,最终通过求解稀疏空间谱获得鲁棒的DOA估计。本文进行了大量的不同混响和加性噪声条件下的仿真实验和实际环境中的DOA估计实验,实验结果表明,本文提出的语者声源DOA估计方法在信噪比5-30dB范围内可获得均方根误差（Root mean square error,RMSE）小于1°的估计精度。在实际环境中也取得了2-10°误差的DOA估计结果。

A robust high resolution speaker source direction of arrival（DOA）estimation method is proposed based on one acoustic vector sensor（AVS）and spatial sparse representation.Under the reverberation and additive noise conditions,the array covariance vector model of the received signals by AVS is first derived.Then the sparse representation model of the covariance vector is developed.Finally the robust DOA estimation is obtained by recovering the sparse vector.A large number of simulation experiments are carried out under different reverberation and additive noise conditions,and also DOA estimation experiments in the actual environment.The results show that the proposed speaker DOA estimation is able to achieve root mean square error（RMSE）of below 1°when SNR is from 5dB to 30 dB and 2—10°error in the real scenario.