A Phase Error Correction Method for Airborne TomoSAR Based on Multi-aperture and Self-Focusing

CHENG Xiaolu; WANG Changcheng; WAN Jie

doi:10.13203/j.whugis20250002

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A Phase Error Correction Method for Airborne TomoSAR Based on Multi-aperture and Self-Focusing

更新时间：2026-11-05

- A Phase Error Correction Method for Airborne TomoSAR Based on Multi-aperture and Self-Focusing
- Geomatics and Information Science of Wuhan University Vol. 50, Issue 8, Pages: 1639-1647(2025)
- 作者机构：
  
  中南大学地球科学与信息物理学院，湖南长沙，410083
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20250002
  CLC： P237
- Received：02 January 2025，
  
  Published：05 August 2025
- 稿件说明：
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程晓璐,汪长城,万杰.一种基于多孔径自聚焦的机载层析SAR相位误差校正方法[J].武汉大学学报（信息科学版）,2025,50(8):1639-1647.

CHENG Xiaolu,WANG Changcheng,WAN Jie.A Phase Error Correction Method for Airborne TomoSAR Based on Multi-aperture and Self-Focusing[J].Geomatics and Information Science of Wuhan University,2025,50(8):1639-1647.
程晓璐,汪长城,万杰.一种基于多孔径自聚焦的机载层析SAR相位误差校正方法[J].武汉大学学报（信息科学版）,2025,50(8):1639-1647. DOI：,10.13203/j.whugis20250002

CHENG Xiaolu,WANG Changcheng,WAN Jie.A Phase Error Correction Method for Airborne TomoSAR Based on Multi-aperture and Self-Focusing[J].Geomatics and Information Science of Wuhan University,2025,50(8):1639-1647. DOI：,10.13203/j.whugis20250002

摘要

在利用机载多基线合成孔径雷达（synthetic aperture radar

SAR）数据开展层析SAR森林参数反演时，轨道不稳定等因素引起的相位误差会改变回波信号相位，导致层析谱出现虚假峰值或目标，甚至产生散焦现象。现有的基于多孔径的相位误差校正方法虽能消除大部分误差相位，但由于其直接采用估计的相位误差进行相位校正，处理后仍存在残余误差。基于信息熵最小化的自聚焦相位校正算法虽可有效校正相位误差、显著提升影像聚焦质量，却因高度依赖初始值选择，易陷入局部最优，难以实现全局最优的相位校正效果。基于此，提出一种联合多孔径与自聚焦算法的机载 SAR 相位误差校正方法。该方法首先利用多孔径方法获取相位误差初值；然后对初值进行自聚焦处理，进一步消除残余相位误差。为验证该方法的有效性与可行性，以机载 P 波段层析SAR 数据为对象，开展相位误差消除实验与分析。实验结果表明，相较于单一的多孔径方法，联合多孔径和自聚焦算法的相位误差校正方法在处理后，残余误差相位显著减少，且校正后的层析谱旁瓣、偏移等问题明显改善，清晰度得到大幅提升。

Abstract

Objectives

When using airborne multi-baseline synthetic aperture radar (SAR) data for the inversion of stratified SAR forest parameters

the phase error caused by the orbit instability and other factors will distort the phase of the echo signal

which will lead to false peaks

target misjudgment

and defocusing of the stratified spectrum. Although the multi-aperture phase error correction method can remove most of the error phases

some errors will still remain after processing because it directly adopts the estimated phase error for correction. Although the self-focusing phase correction algorithm based on information entropy minimization can effectively correct the phase error and significantly improve the image focusing quality

it has the limitations of relying on the selection of the initial value and easily falling into the local optimal solution

which makes it difficult to realize the globally optimal phase correction effect. The aim is to develop an efficient airborne SAR phase error correction method to improve the accuracy and reliability of stratified SAR forest parameter inversion.

Methods

A two-step phase error correction method combining the multi-aperture and self-focusing algorithms is proposed. First

the initial value of phase error is estimated by mining the redundant information of multi-baseline SAR data using the multi-aperture method. Second

the estimated initial value is used as the input of the self-focusing algorithm to iteratively optimize the phase error based on the principle of information entropy minimization to effectively eliminate the residual error.

Results

Compared to the multi-aperture method

the on-board SAR phase error correction method utilizing the combined multi-aperture and self-focusing algorithms has significantly fewer error phases after correction. At the same time

the corrected chromatographic spectra have significantly fewer side flaps and offsets than the multi-aperture method

and the clarity is higher.

Conclusions

The airborne SAR phase error correction algorithm combining multi-aperture and self-focusing overcomes the limitations of a single method

combining the robustness of multi-aperture correction with the high accuracy of self-focusing optimization. The method provides a reliable solution for airborne stratified SAR phase error correction

which is especially suitable for complex forest scenarios and lays a solid foundation for more accurate forest parameter inversion.

关键词

Keywords

references

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