基于KR积的稀疏重构近场源定位  

Near-Field Sound Source Localization via Sparse Reconstruction Based on KR Product

作　　者：窦育强 王晖[2] DOU Yu-qiang;WANG Hui(Big Data Engineering Laboratory for Teaching Resources&Assessment of Education Quality,Henan Normal University Xinxiang Henan 453007;Key laboratory Media Audio,Communication University of China Chaoyang Beijing 100024)

机构地区：[1]河南师范大学教学资源与教育质量评估大数据河南省工程实验室,河南新乡453007 [2]中国传媒大学媒介音视频教育部重点实验室,北京朝阳区100024

出　　处：《电子科技大学学报》2019年第6期845-849,共5页Journal of University of Electronic Science and Technology of China

基　　金：国家自然科学基金(61231015)

摘　　要：针对声源数多于阵元数的近场信源定位问题,该文提出一种基于Khatri-Rao(KR)积的稀疏重构近场源定位方法.该方法首先假设信号是准平稳的,然后通过KR积得到虚拟阵列结构,增加了阵列的自由度;接着在虚拟阵列结构下对虚拟信号进行稀疏表示,最后通过l1范数约束得到声源的空间谱估计.仿真表明,此稀疏重构定位方法可以实现信源定位的欠定估计,且性能优于基于KR积的子空间方法.Aiming at the problem of near-field sound source localization estimation under the condition of less array elements than sources, the method of sparse reconstruction based on Khatri-Rao (KR) product is proposed. The source signals are wide-sense quasi-stationary in this method. A virtual array structure is acquired by KR product and the degree of freedom is increased. In the virtual array structure the spectra of the sound sources are acquired band on sparse reconstruction, which is solved by l1 norm method. Simulations demonstrate the proposed method can realized underdeterminded estimation of sound source and the performance is better than the subspace method.

关 键 词：KR积 L1范数 近场 源定位 稀疏重构 

分 类 号：TN912[电子电信—通信与信息系统]