Spatial Difference Smoothing Algorithm for DOA Estimation of Coherent Sources in the Presence of Colored Noise Fields

QI Chong-ying; WANG Yong-liang; ZHANG Yong-shun; CHEN Hui

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Spatial Difference Smoothing Algorithm for DOA Estimation of Coherent Sources in the Presence of Colored Noise Fields

更新时间：2025-07-16

- Spatial Difference Smoothing Algorithm for DOA Estimation of Coherent Sources in the Presence of Colored Noise Fields
- Acta Electronica Sinica Vol. 33, Issue 7, Pages: 1314-1318(2005)
- 作者机构：
  
  1. 空军工程大学导弹学院,陕西三原,713800
  2. 空军雷达学院重点实验室,湖北,武汉,430010
  3. 空军工程大学导弹学院陕西三原,713800
  4. 空军雷达学院重点实验室湖北武汉,430010
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN911.7
- Published：2005
- 稿件说明：
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QI Chong-ying, WANG Yong-liang, ZHANG Yong-shun, et al. Spatial Difference Smoothing Algorithm for DOA Estimation of Coherent Sources in the Presence of Colored Noise Fields[J]. Acta Electronica Sinica, 2005, 33(7): 1314-1318.
DOI：

QI Chong-ying, WANG Yong-liang, ZHANG Yong-shun, et al. Spatial Difference Smoothing Algorithm for DOA Estimation of Coherent Sources in the Presence of Colored Noise Fields[J]. Acta Electronica Sinica, 2005, 33(7): 1314-1318. DOI：

摘要

文中提出了一种色噪声背景下相干信源波达方向(DOA)估计的新算法-空间差分平滑(SDS)算法.SDS算法利用均匀线阵协方差矩阵的Toeplitz分解特性

差分平滑运算

将非相干信源与相关(或相干)信源分开分辨

从而重复利用阵列接收数据

可分辨更多信源.SDS算法可对消空间色噪声

适用于更广泛的未知噪声背景及低信噪比环境.相比常规谱估计算法

SDS算法具有更强的信源过载能力及阵元节省能力

利用少数阵元进行迭代空间平滑运算

还可明显减小SDS算法的计算量.计算机仿真结果证明了SDS算法理论的正确性和有效性.

Abstract

A new algorithm is proposed for direction of arrival (DOA) estimation of coherent sources in the presence of colored noise fields

which is called "Spatial Difference Smoothing (SDS)" method.By exploiting the property of Teoplitz decomposition of the autocovariance matrix

the SDS method resolves the correlated sources and incoherent sources separately.In this way the output data of the array are used repeatedly

and more sources can be estimated.The SDS method can fully eliminate spatially colored noise

and fit for more general unknown noise fields and low SNR environments.Compared with the conventional methods

the SDS can resolve more sources using the same number of sensors.In addition

the SDS method performs spatial smoothing iteratively utilizing smaller sensor arrays and has smaller computational complexity.Computer simulation results verify the correctness and effectiveness of the proposed SDS method.

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