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

JIANG Rui; ZHU Dai-yin; SHEN Ming-wei; ZHU Zhao-da

doi:10.3969/j.issn.0372-2112.2012.06.031

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

更新时间：2025-07-16

- An Autofocus Algorithm for Spotlight SAR Imagery Using the Projection Approximation Subspace Tracking Approach
- Acta Electronica Sinica Vol. 40, Issue 6, Pages: 1251-1256(2012)
- 作者机构：
  
  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
  3. 南京航空航天大学信息科学工程学院江苏南京,210016
  4. 南京河海大学计算机与信息学院江苏南京,211100
- 作者简介：
- 基金信息：
  
  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)
- DOI：10.3969/j.issn.0372-2112.2012.06.031
  CLC： TN957.51
- Published：2012
- 稿件说明：
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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, 2012, 40(6): 1251-1256.
DOI：

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, 2012, 40(6): 1251-1256. DOI： 10.3969/j.issn.0372-2112.2012.06.031.

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.

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.

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