基于投影近似子空间跟踪技术的自聚焦算法

doi:10.3969/j.issn.0372-2112.2012.06.031

电子学报

基于投影近似子空间跟踪技术的自聚焦算法

蒋锐¹, 朱岱寅¹, 沈明威², 朱兆达¹

1. 南京航空航天大学信息科学工程学院,江苏南京 210016;
2. 南京河海大学计算机与信息学院,江苏南京 211100

收稿日期:2010-11-23 修回日期:2011-09-26 出版日期:2012-06-25
- 作者简介:
- 蒋锐 男,1985年1月出生于江苏南京.2007年本科毕业于南京航空航天大学信息工程专业.现为南航电子工程系博士研究生,主要研究方向为雷达信号处理. E-mail:jr_ray@yahoo.cn
  朱岱寅 男,1974年7月出生于江苏无锡.1996年本科毕业于东南大学无线电工程系,分别于1998年和2002年在南京航空航天大学电子工程系获硕士和博士学位.现为南航电子工程系教授、博士生导师,IEEE会员,主要从事雷达成像和信号处理方面的研究. E-mail:zhudy@nuaa.edu.cn
- 基金资助:
- 国家自然科学基金 (No.61071165); 教育部新世纪优秀人才支持计划 (No.NCET-09-0069)资助课题; 航空科学基金 (No.20102052024)

An Autofocus Algorithm for Spotlight SAR Imagery Using the Projection Approximation Subspace Tracking Approach

JIANG Rui¹, ZHU Dai-yin¹, SHEN Ming-wei², ZHU Zhao-da¹

1. College of Electronics and Information Engineering,Nanjing University of Aeronautics & Astronautics,Nanjing,Jiangsu 210016,China;
2. College of Computer & Information,Hohai University,Nanjing,Jiangsu 211100,China

Received:2010-11-23 Revised:2011-09-26 Online:2012-06-25 Published:2012-06-25
- Supported by:
- National Natural Science Foundation of China (No.61071165); Program for New Century Excellent Talents in University of Ministry of Education of China (No.NCET-09-0069); Aeronautical Science Foundation of China, ASFC (No.20102052024)

摘要/Abstract

摘要： 基于特征向量法的自聚焦算法具有比相位梯度自聚焦(Phase Gradient Autofocus,简称PGA)算法更好的算法性能,但该算法必须对协方差矩阵进行特征分解,所以运算量大.利用投影近似子空间跟踪(Projection Approximation Subspace Tracking,简称PAST)技术的自聚焦算法可以解决上述问题.通过实际数据处理结果对比,证明基于PAST技术的自聚焦算法是一种可满足实时处理要求的有效自聚焦方法.

关键词: 相位梯度自聚焦(PGA), 特征向量法, 特征分解, 投影近似子空间跟踪(PAST)

Abstract: The eigenvector method for maximum-likelihood estimation of phase error has better algorithmic performance than phase gradient autofocus (PGA).However,this method requires eigendecomposition of the sample covariance matrix,which is a computationally expensive task and also limits the real-time application.In order to overcome such difficulty,an autofocus algorithm using the projection approximation subspace tracking (PAST) approach is proposed.With this method,the procedures of covariance matrix estimation and eigendecomposition can be avoided and the computational cost can be reduced to the level of that of PGA.Monte Carlo tests and real SAR data validate that the new approach outperforms PGA.

Key words: phase gradient autofocus (PGA), eigenvector method, eigendecomposition, projection approximation subspace tracking (PAST)

中图分类号:

TN957.51

蒋锐, 朱岱寅, 沈明威, 等. 基于投影近似子空间跟踪技术的自聚焦算法[J]. 电子学报, DOI: 10.3969/j.issn.0372-2112.2012.06.031.

JIANG Rui, ZHU Dai-yin, SHEN Ming-wei, et al. An Autofocus Algorithm for Spotlight SAR Imagery Using the Projection Approximation Subspace Tracking Approach[J]. Acta Electronica Sinica, DOI: 10.3969/j.issn.0372-2112.2012.06.031.

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基于投影近似子空间跟踪技术的自聚焦算法

An Autofocus Algorithm for Spotlight SAR Imagery Using the Projection Approximation Subspace Tracking Approach

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