Mixed Far-Field and Near-Field Source Localization Algorithm Based on Reweighted l<sub>1</sub>-Norm Penalty

TIAN Ye; LIAN Qiu-sheng

doi:10.3969/j.issn.0372-2112.2016.10.023

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Mixed Far-Field and Near-Field Source Localization Algorithm Based on Reweighted l₁-Norm Penalty

更新时间：2025-07-16

- Mixed Far-Field and Near-Field Source Localization Algorithm Based on Reweighted l₁-Norm Penalty
- Acta Electronica Sinica Vol. 44, Issue 10, Pages: 2440-2448(2016)
- 作者机构：
  
  燕山大学信息科学与工程学院,河北,秦皇岛,066004
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2016.10.023
  CLC： TN911.7
- Published：2016
- 稿件说明：
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TIAN Ye, LIAN Qiu-sheng. Mixed Far-Field and Near-Field Source Localization Algorithm Based on Reweighted l₁-Norm Penalty[J]. Acta Electronica Sinica, 2016, 44(10): 2440-2448.
DOI：

TIAN Ye, LIAN Qiu-sheng. Mixed Far-Field and Near-Field Source Localization Algorithm Based on Reweighted l₁-Norm Penalty[J]. Acta Electronica Sinica, 2016, 44(10): 2440-2448. DOI： 10.3969/j.issn.0372-2112.2016.10.023.

摘要

现有信源定位方法大多假定信源是远场源或近场源，而实际定位系统中往往存在远场源和近场源共存的情况.为实现远、近场源分离及高精度信源定位，本文在稀疏信号重构理论框架下提出了一种新的远近场混合源定位算法.该算法利用阵列协方差矩阵反对角线元素和重加权

范数惩罚获得所有信源的到达角（Direction Of Arrival，DOA）估计.在DOA估计的基础上，根据远场与近场源距离参数位于不同区间的特点利用一维搜索实现远、近场源分离以及近场源距离参数的估计.从理论角度分析了重加权

范数惩罚算法的重构性能.本文所提算法不仅同时适用于高斯和非高斯信号，而且无需多维搜索和参数配对，也无需信源数的先验信息，同时还可以获得较好的定位精度.计算机仿真结果验证了所提算法的有效性.

Abstract

Existing source localization methods mostly assume that the sources are pure near-field sources or pure far-field sources.While in practical localization systems

both far-field and near-field sources may exist simultaneously.To classify far-field and near-field sources

and also to achieve high-precision source localization

a novel mixed far-field and near-field source localization algorithm is proposed in sparse signal reconstruction framework.The algorithm first utilizes anti-diagonal elements of array covariance matrix and reweighted

-norm penalty to obtain DOA estim

ation of all sources

then classifies far-field and near-field sources and successively obtains range estimation of near-field sources via one-dimensional search

by exploring the feature that the range parameters of far-field and near-field sources are located in different areas.Theoretically

we analyze the reconstruction performance of the reweighted

-norm penalty algorithm.The proposed algorithm is not only suitable for dealing with Gaussian signals and non-Gaussian signals

but also without multi-dimensional search and parameter pairing process

and also without knowing the number of sources.Meanwhile

the proposed algorithm can even provide good estimation accuracy.Computer simulation results validate the effectiveness of the proposed algorithm.

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Mixed Far-Field and Near-Field Source Localization Algorithm Based on Reweighted l1-Norm Penalty

DOI：10.3969/j.issn.0372-2112.2016.10.023

摘要

Abstract

关键词

Keywords

references

Mixed Far-Field and Near-Field Source Localization Algorithm Based on Reweighted l₁-Norm Penalty