Statistical Analysis of Low Dimensional Subspace Techniques for the Resoluton of Coherent Sources

Lu Ming; Bao Zheng

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Statistical Analysis of Low Dimensional Subspace Techniques for the Resoluton of Coherent Sources

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- Statistical Analysis of Low Dimensional Subspace Techniques for the Resoluton of Coherent Sources
- Acta Electronica Sinica Issue 1, Pages: 79-85(1990)
- 作者机构：
  
  西安电子科技大学
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：1990
- 稿件说明：
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[1]路鸣,保铮.用低维子空间法分辨空间相干源的统计分析[J].电子学报,1990(01):79-85.

Lu Ming, Bao Zheng. Statistical Analysis of Low Dimensional Subspace Techniques for the Resoluton of Coherent Sources[J]. Acta Electronica Sinica, 1990, (1): 79-85.
[1]路鸣,保铮.用低维子空间法分辨空间相干源的统计分析[J].电子学报,1990(01):79-85. DOI：

Lu Ming, Bao Zheng. Statistical Analysis of Low Dimensional Subspace Techniques for the Resoluton of Coherent Sources[J]. Acta Electronica Sinica, 1990, (1): 79-85. DOI：

摘要

八十年代以来

Schmidt提出的基于特征矢量空间的多信号分类（MUSIC）法受到广泛关洼。Evans

Shan等采用取较短的子阵列

并作空间平滑处理

将MUSIC法推广到相干源的场合。文献[7]、[10]、[14]从数据矩阵出发

提出了构造低维子空间以分辨相干源的一般方法。在诸多方法中

它们的分辨性能如何

以及怎样进一步提高分辨率需要深入研究

本文通过统计分析研究了上述问题。

Abstract

The eigendecomposition-based MUSIC technique of Schmidt has received considerable attention since 1980’. Evans

Shan

and others applied MUSIC algorithm to the case of coherent sources by using spatial smoothing technique across the array.References[7]、[10]、 [14]introduced a general procedure to estimate low-dimensional subspace for the high-resolution of coherent signal sources. This paper studies those high-resolution methods statistically.

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