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中国科学院大学学报 ›› 2024, Vol. 41 ›› Issue (1): 88-96.DOI: 10.7523/j.ucas.2022.053

• 电子信息与计算机科学 • 上一篇    下一篇

一种改进的基于干涉相位周期的无人机差分干涉合成孔径雷达基线估计方法

张桐1,2, 乔明1, 党相卫3, 钟声依柳1,2   

  1. 1. 中国科学院空天信息创新研究院, 北京 100190;
    2. 中国科学院大学电子电气与通信工程学院, 北京 100049;
    3. 北京理工雷科电子信息技术有限公司, 北京 100081
  • 收稿日期:2022-03-18 修回日期:2022-05-06 发布日期:2022-05-12
  • 通讯作者: 张桐,E-mail:zhangtong193@mails.ucas.ac.cn
  • 基金资助:
    国家自然科学基金(41874059)资助

An improved UAV-borne DInSAR baseline estimation method based on interferometric phase period

ZHANG Tong1,2, QIAO Ming1, DANG Xiangwei3, ZHONG Shengyiliu1,2   

  1. 1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China;
    2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;
    3. Beijing Racobit Electronic Information Technology Co. Ltd, Beijing 100081, China
  • Received:2022-03-18 Revised:2022-05-06 Published:2022-05-12

摘要: 基线在差分干涉处理中是一个至关重要的参数,直接关系到干涉测量的精度。相比于星载平台,无人机在飞行过程中,飞行航迹难以保持高度重合,飞行轨迹和姿态不稳定,给无人机差分干涉SAR基线估计带来极大的困难。从无人机平台的特点和应用场景出发,提出一种改进的基线估计的方法,该方法通过干涉几何关系推导出干涉相位周期、斜距、2点之间距离、相位差的关系,在雷达图像计算特定2点之间距离,然后代入已知参数利用最小二乘法进行基线估计。对比原方法及改进方法的仿真结果及实际数据的基线估计结果,表明改进方法的基线估计结果的精度和鲁棒性有显著提高。

关键词: 无人机, 差分干涉, 基线估计, 微型SAR

Abstract: UAV (unmanned aerial vehicle)-borne differential interferometric synthetic aperture radar (SAR) has unique advantage and has attracted the attention of key research institutions at home and abroad in recent years. Baseline is a crucial parameter in differential in DInSAR processing, which is directly related to the accuracy of interferometric measurements. Compared with space-borne InSAR, UAV platform is difficult to keep the flight at the same track, and the flight attitude are unstable, which brings great difficulties to the baseline estimation of UAV-borne DInSAR. In this paper, starts from the characteristics of UAV platform and application scenarios, an improved baseline estimation method is proposed. This method derives the relationship between the interference phase period, the slant range, the distance between two ground points, and the phase difference through the interference geometric relationship. The distance between the two points is then substituted into the known parameters for baseline estimation using the least squares method. Comparing the simulation results and the actual data results of the original method and the improved method, it shows that the accuracy and robustness of the baseline estimation results of the improved method are significantly improved.

Key words: UAV, DInSAR, baseline estimation, mini-SAR

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