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.
作者
窦育强
王晖
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)
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
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)
关键词
KR积
L1范数
近场
源定位
稀疏重构
KR product
l1 norm
near-field
source localization
sparse reconstruction
