| 引用本文： |
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李亚军,陈焕煜,史意乔,等.基于稀疏对称十字阵列的低复杂度近场多信源定位算法[J].电讯技术，2025，（8）：1281 - 1289. [点击复制]
- LI Yajun,CHEN Huanyu,SHI Yiqiao,et al.A Low-complexity Near-field Multi-source Localization Algorithm with Sparse Symmetric Cross Array[J].，2025，（8）：1281 - 1289. [点击复制]
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| 摘要: |
| 针对多个信源定位中存在的谱峰搜索维度较大、算法运算量大、参数无法自动配对等问题，建立了基于稀疏对称十字阵列(Sparse Symmetric Cross Array,SSCA)的近场多信源信号接收模型，并提出了针对该模型的低复杂度降维多信号分类(Reduced-dimension Multiple Signal Classification,RD-MUSIC)算法。SSCA结构具有中心对称的互素稀疏线阵结构。RD-MUSIC算法利用阵列结构的对称性，通过构造连接矩阵，将三维搜索转换成多个一维搜索，降低了算法的复杂度。该算法仅需2K+1次一维搜索就可以实现獽个信源的定位，且能自动匹配多个信源的角度和距离参数。仿真结果表明,在相同的阵列结构下，与经典三维MUSIC算法相比，所提算法的复杂度降低了5~6个数量级；在相同阵元数量下，与均匀对称十字阵列相比，SSCA结构能够输出更为明显的谱峰，提高了空间分辨率，且其定位结果的均方根误差更小。 |
| 关键词: 近场信源定位 多信源定位 改进MUSIC算法 稀疏对称十字阵列 |
| DOI：10.20079/j.issn.1001-893x.240410002 |
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| 基金项目: |
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| A Low-complexity Near-field Multi-source Localization Algorithm with Sparse Symmetric Cross Array |
| LI Yajun,CHEN Huanyu,SHI Yiqiao,WU Haowei |
| (1.Southwest China Institute of Electronic Technology,Chengdu 610036,China;2.School of Microelectronic and Communication Engineering,Chongqing University,Chongqing 400044,China) |
| Abstract: |
| For the problems of large dimension of spectral peak search,high complexity of algorithms and inability of automatic parameter matching in three-dimensional(3D) localization of multiple sources in near field,the authors establish a multi-source reception model based on sparse symmetric cross array(SSCA) and propose a low-complexity reduced-dimensional multiple signal classification(RD-MUSIC) algorithm based on this model.The SSCA is a linear centrosymmetric structure with coprime and sparse elements.The RD-MUSIC algorithm uses the symmetry of the array structure and converts the 3D search into multiple one-dimensional(1D) searches by constructing a connection matrix,which reduces the complexity of the algorithm.The proposed algorithm needs only 2獽+1 1D searches to achieve the localization of 獽 sources,and it can automatically match the angle and distance parameters of multiple sources.The simulation results show that,with the same array structure,the complexity of the proposed algorithm is reduced by 5~6 orders of magnitude,compared with that the classical 3D-MUSIC algorithm.With the same number of array elements,the proposed SSCA structure not only improves the spatial resolution with obvious spectral peaks,but also makes the localization results more accurate with smaller root mean square errors,compared with the uniform symmetric cross array. |
| Key words: near-field source location multi-source localization improved MUSIC algorithm sparse symmetric cross array |
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