Three-Dimensional Localization of Mixed Near-Field and Far-Field Sources Based on a Unified Exact Propagation Model

被引:1
|
作者
Fang, Jiaxiong [1 ]
Chen, Hua [1 ]
Liu, Wei [2 ]
Yang, Songjie [3 ]
Yuen, Chau [4 ]
So, Hing Cheung [5 ]
机构
[1] Ningbo Univ, Fac Elect Engn & Comp Sci, Ningbo 315211, Peoples R China
[2] Hong Kong Polytech Univ, Dept Elect & Elect Engn, Hong Kong, Peoples R China
[3] Univ Elect Sci & Technol China, Natl Key Lab Wireless Commun, Chengdu 610054, Peoples R China
[4] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
[5] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China
基金
中国国家自然科学基金; 英国工程与自然科学研究理事会; 新加坡国家研究基金会;
关键词
Noise measurement; Location awareness; Classification algorithms; Geometry; Three-dimensional displays; Signal processing algorithms; Phased arrays; Multiple signal classification; Apertures; Microphone arrays; Array signal processing; source localization; spatial-temporal; near-field; far-field; PASSIVE LOCALIZATION; SOURCE ENUMERATION; ARRAY; CLASSIFICATION; LOCATION; SIGNALS; ESPRIT;
D O I
10.1109/TSP.2024.3520551
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In applications like speaker localization using a microphone array, the collected signals are typically a mixture of far-field (FF) and near-field (NF) sources. To find the positions of both NF and FF sources, a three-dimensional spatial-temporal localization algorithm based on a unified exact propagation geometry is developed in this paper, which avoids approximating the spatial phase difference with the first-order and second-order Taylor expansions applied to FF and NF sources, respectively. Our scheme utilizes cross-correlation to produce virtual observations for establishing a third-order parallel factor data model with the use of spatial and temporal information. The array's steering vectors can be extracted by trilinear decomposition. The amplitude and phase information of the whole array elements is jointly exploited to classify the source types and obtain the location estimates via a least squares method. Moreover, the proposed algorithm is computationally efficient since no spectral searches, high-order statistics calculations or parameter pairing procedures are required. The deterministic Cram & eacute;r-Rao bound is also derived as a performance benchmark, and numerical results are provided to demonstrate the effectiveness of the developed method.
引用
收藏
页码:245 / 258
页数:14
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