Near-Field DOA Estimation Algorithm Using Array Partition

CHEN Guang-hui; ZENG Xiao-ping; JIAO Shuang

doi:10.12263/DZXB.20201364

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Near-Field DOA Estimation Algorithm Using Array Partition

PAPERS | 更新时间：2025-12-08

- Near-Field DOA Estimation Algorithm Using Array Partition
- ACTA ELECTRONICA SINICA Vol. 50, Issue 9, Pages: 2110-2118(2022)
- 作者机构：
  
  1.重庆大学微电子与通信工程学院,重庆 400044
  2.中国大唐集团科学技术研究院有限公司中南电力试验研究院,河南郑州 450000
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20201364
  CLC： TN911.23
- Received：01 December 2020，
  
  Revised：2021-08-31，
  
  Published：25 September 2022
- 稿件说明：
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陈光辉,曾孝平,焦爽.基于阵列划分的近场DOA估计算法[J].电子学报,2022,50(09):2110-2118.

CHEN Guang-hui,ZENG Xiao-ping,JIAO Shuang.Near-Field DOA Estimation Algorithm Using Array Partition[J].ACTA ELECTRONICA SINICA,2022,50(09):2110-2118.
陈光辉,曾孝平,焦爽.基于阵列划分的近场DOA估计算法[J].电子学报,2022,50(09):2110-2118. DOI： 10.12263/DZXB.20201364.

CHEN Guang-hui,ZENG Xiao-ping,JIAO Shuang.Near-Field DOA Estimation Algorithm Using Array Partition[J].ACTA ELECTRONICA SINICA,2022,50(09):2110-2118. DOI： 10.12263/DZXB.20201364.

摘要

该文提出一种利用阵列划分的近场到达方向（Direction Of Arrival，DOA）估计算法.首先，通过划分阵列和四阶累积量构造一个仅包含DOA信息的托普利兹矩阵；然后，由于多重信号分类（Multiple Signal Classification，MUSIC）算法的空间谱在谱峰处是一个断点、且趋近较大值，因此，为得到高分辨率谱峰，该算法通过求解空间谱的一阶导数构造一个新 “空间谱”；最后，通过一维MUSIC算法估计近场信源的距离.仿真结果表明，该算法在低信噪比下提高了DOA的估计精度，且不需要二维谱峰搜索，只需

K+

1次一维谱峰搜索.

Abstract

This paper proposes a near-field direction of arrival(DOA) estimation algorithm by using array partition. Firstly

the fourth-order cumulant and array partition are used to construct a Toeplitz matrix with only DOA information. Secondly

the spatial spectrum of multiple signal classification(MUSIC) algorithm at the spectrum peak is a breakpoint and approaches a larger value. Thus

to obtain high-resolution spectral peak

the first derivative of spatial spectrum is used to construct a new ‘spatial spectrum’. Finally

the range of near-field source is estimated by the one-dimensional MUSIC algorithm. The simulation results show that the proposed algorithm improves the accuracy of DOA estimation at low signal to noise ratio. In addition

the proposed algorithm does not require two-dimensional spectral peak search

and it requires

K+

1 one-dimensional spectral peak search.

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references

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