Hua Chen;Zelong Yi;Zhiwei Jiang;Wei Liu;Ye Tian;Qing Wang;Gang Wang
{"title":"Spatial-Temporal-Based Underdetermined Near-Field 3-D Localization Employing a Nonuniform Cross Array","authors":"Hua Chen;Zelong Yi;Zhiwei Jiang;Wei Liu;Ye Tian;Qing Wang;Gang Wang","doi":"10.1109/JSTSP.2024.3400046","DOIUrl":null,"url":null,"abstract":"In this paper, an underdetermined three-dimensional (3-D) near-field source localization method is proposed, based on a two-dimensional (2-D) symmetric nonuniform cross array. Firstly, by utilizing the symmetric coprime array along the x-axis, a fourth-order cumulant (FOC) based matrix is constructed, followed by vectorization operation to form a single virtual snapshot, which is equivalent to the received data of a virtual array observing from virtual far-field sources, generating an increased number of degrees of freedom (DOFs) compared to the original physical array. Meanwhile, multiple delay lags, named as pseudo snapshots, are introduced to address the single snapshot issue. Then, the received data of the uniform linear array along the y-axis is similarly processed to form another virtual array, followed by a cross-correlation operation on the virtual array observations constructed from the coprime array. Finally, the 2-D angles of the near-field sources are jointly estimated by employing the recently proposed sparse and parametric approach (SPA) and the Vandermonde decomposition technique, eliminating the need for parameter discretization. To estimate the range term, the conjugate symmetry property of the signal's autocorrelation function is used to construct the second-order statistics based received data with the whole array elements, and subsequently, the one-dimensional (1-D) MUSIC algorithm is applied. Moreover, some properties of the proposed array are analyzed. Compared with existing algorithms, the proposed one has better estimation performance given the same number of sensor elements, which can work in an underdetermined and mixed sources situation, as shown by simulation results with 3-D parameters automatically paired.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 4","pages":"561-571"},"PeriodicalIF":8.7000,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10529521/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, an underdetermined three-dimensional (3-D) near-field source localization method is proposed, based on a two-dimensional (2-D) symmetric nonuniform cross array. Firstly, by utilizing the symmetric coprime array along the x-axis, a fourth-order cumulant (FOC) based matrix is constructed, followed by vectorization operation to form a single virtual snapshot, which is equivalent to the received data of a virtual array observing from virtual far-field sources, generating an increased number of degrees of freedom (DOFs) compared to the original physical array. Meanwhile, multiple delay lags, named as pseudo snapshots, are introduced to address the single snapshot issue. Then, the received data of the uniform linear array along the y-axis is similarly processed to form another virtual array, followed by a cross-correlation operation on the virtual array observations constructed from the coprime array. Finally, the 2-D angles of the near-field sources are jointly estimated by employing the recently proposed sparse and parametric approach (SPA) and the Vandermonde decomposition technique, eliminating the need for parameter discretization. To estimate the range term, the conjugate symmetry property of the signal's autocorrelation function is used to construct the second-order statistics based received data with the whole array elements, and subsequently, the one-dimensional (1-D) MUSIC algorithm is applied. Moreover, some properties of the proposed array are analyzed. Compared with existing algorithms, the proposed one has better estimation performance given the same number of sensor elements, which can work in an underdetermined and mixed sources situation, as shown by simulation results with 3-D parameters automatically paired.
本文提出了一种基于二维对称非均匀交叉阵列的欠定三维近场源定位方法。首先,利用沿 x 轴对称共轭阵列,构建基于四阶累积(FOC)的矩阵,然后进行矢量化操作,形成单个虚拟快照,该快照相当于虚拟阵列从虚拟远场源观测到的接收数据,与原始物理阵列相比,增加了自由度(DOF)。同时,为了解决单快照问题,还引入了多个延迟滞后(称为伪快照)。然后,对沿 Y 轴的均匀线性阵列的接收数据进行类似处理,形成另一个虚拟阵列,接着对由共轭阵列构建的虚拟阵列观测数据进行交叉相关操作。最后,利用最近提出的稀疏和参数方法(SPA)以及范德蒙德分解技术共同估算近场源的二维角度,从而消除了参数离散化的需要。为了估算测距项,利用信号自相关函数的共轭对称特性来构建基于整个阵元接收数据的二阶统计量,然后应用一维(1-D)MUSIC 算法。此外,还分析了拟议阵列的一些特性。三维参数自动配对的仿真结果表明,与现有算法相比,拟议算法在相同传感元件数量的情况下具有更好的估计性能,可以在不确定和混合信号源的情况下工作。
期刊介绍:
The IEEE Journal of Selected Topics in Signal Processing (JSTSP) focuses on the Field of Interest of the IEEE Signal Processing Society, which encompasses the theory and application of various signal processing techniques. These techniques include filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals using digital or analog devices. The term "signal" covers a wide range of data types, including audio, video, speech, image, communication, geophysical, sonar, radar, medical, musical, and others.
The journal format allows for in-depth exploration of signal processing topics, enabling the Society to cover both established and emerging areas. This includes interdisciplinary fields such as biomedical engineering and language processing, as well as areas not traditionally associated with engineering.