A Second-Order Performance Analysis of the ESPRIT Algorithm in Presence of Array Modeling Errors

WANG Ding; YAO Hui; WU Ying

doi:10.3969/j.issn.0372-2112.2012.10.039

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A Second-Order Performance Analysis of the ESPRIT Algorithm in Presence of Array Modeling Errors

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- A Second-Order Performance Analysis of the ESPRIT Algorithm in Presence of Array Modeling Errors
- Acta Electronica Sinica Vol. 40, Issue 10, Pages: 2133-2139(2012)
- 作者机构：
  
  解放军信息工程大学信息工程学院,河南,郑州,450002
- 作者简介：
- 基金信息：
  
  Doctoral Dissertation Innovation Fund of Information Engineering University (No.BSLWCX200801);Peking University School of Electronic and Computer Engineering Future Development Fund (No.YP12JJ202057)
- DOI：10.3969/j.issn.0372-2112.2012.10.039
  CLC： TN911.7
- Published：2012
- 稿件说明：
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WANG Ding, YAO Hui, WU Ying. A Second-Order Performance Analysis of the ESPRIT Algorithm in Presence of Array Modeling Errors[J]. Acta Electronica Sinica, 2012, 40(10): 2133-2139.
DOI：

WANG Ding, YAO Hui, WU Ying. A Second-Order Performance Analysis of the ESPRIT Algorithm in Presence of Array Modeling Errors[J]. Acta Electronica Sinica, 2012, 40(10): 2133-2139. DOI： 10.3969/j.issn.0372-2112.2012.10.039.

摘要

针对阵列模型误差扰动的影响

该文对ESPRIT算法的方位估计性能进行二阶性能分析(即忽略模型误差扰动量的三次及以上各项).与经典分析方法(即一阶分析方法)相比

文中的性能分析方法能够在模型误差扰动较大的情况下提高性能预测精度.此外

文中的理论推导不仅针对阵列流形失配的情况

还针对阵元噪声模型失配的情形.数值实验表明:在较大模型误差的条件下

文中的二阶性能分析方法能够提高对ESPRIT算法的性能预测精度.

Abstract

Aiming at the effects of the array modeling errors

the second-order performance analysis method (i.e.

discarding the third-order and higher-order terms of the modeling errors) for the ESPRIT algorithm is proposed in this paper.Compared to the classical analysis method (i.e.

the first-order analysis method)

the second-order analysis method can yield higher performance prediction precision

especially in the presence of large modeling errors.In addition

the presented analysis method is not only suitable for the case of the array manifold mismatch

but also applies to the case of the sensor noise model mismatch.The numerical experiments verify the superiority of the second-order performance analysis method in the presence of large modeling errors.

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