A Symmetric Super Transformed Nested Array for Localization of Mixed Near Field and Far Field Sources With Reduced Mutual Coupling.
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| Title: | A Symmetric Super Transformed Nested Array for Localization of Mixed Near Field and Far Field Sources With Reduced Mutual Coupling. |
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| Authors: | Guo, Yiming1 (AUTHOR), Ding, Dongyan2 (AUTHOR), Mei, Fengtong3 (AUTHOR) meifengtong@xidian.edu.cn, Liu, Qian4 (AUTHOR), Xu, Haiyun5 (AUTHOR), Habib, Mohammad Rezwan (AUTHOR) mohabib@wiley.com |
| Source: | International Journal of Antennas & Propagation. 11/30/2025, Vol. 2025, p1-13. 13p. |
| Subjects: | Degrees of freedom, Direction of arrival estimation, Automatic tracking, Sparse matrices |
| Abstract: | Recently, symmetric sparse uniform linear arrays (SSULAs) have been widely used for mixed far‐field (FF) and near‐field (NF) source localization owing to their high degrees of freedom and large array aperture. However, the heavy levels of mutual coupling of SSULAs limit the accuracy of source localization. In this article, a novel sparse array structure called symmetric super transformed nested array (SSTNA) is designed for mixed source localization, which combines the advantages of high degrees of freedom and the ability to tolerate strong mutual coupling. The SSTNA can be generated by two steps. First, we switch the position of the two subarrays within the nested array and relocate the sensors of dense subarray. Secondly, the whole array is flipped utilizing the zeros as symmetry point. We provide an analytical expression for the proposed array and derive expressions of its degrees of freedom and weight functions. Numerical results demonstrate that SSTNA is superior to existing sparse arrays in both direction of arrival (DOA) and distance estimation. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Antennas & Propagation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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