基于特征值分解的多平台合成孔径定位方法

习梦婷; 王裕旗; 王安义

doi:10.11959/j.issn.1000-0801.2025206

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基于特征值分解的多平台合成孔径定位方法

研究与开发 | 更新时间：2026-01-20

- 基于特征值分解的多平台合成孔径定位方法
- An eigenvalue decomposition based multi-platform synthetic aperture positioning method
- 电信科学 2025年41卷第11期页码：131-141
- 作者机构：
  
  西安科技大学通信与信息工程学院，陕西西安 710054
- 作者简介：
  
  [ "习梦婷（2002- ），女，西安科技大学通信与信息工程学院硕士生，主要研究方向为合成孔径雷达、信号处理和无源定位等。" ]
  [ "王裕旗（1994- ），男，博士，西安科技大学通信与信息工程学院副教授，主要研究方向为合成孔径雷达成像、信号处理和无源定位等。" ]
  [ "王安义（1968- ），男，博士，西安科技大学通信与信息工程学院博士生导师，主要研究方向为宽带数字移动通信关键技术、智能信息处理技术和煤矿智能优化等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62401459;62222113)
- DOI：10.11959/j.issn.1000-0801.2025206
  中图分类号： TN911.7
- 收稿：2025-03-04，
  
  修回：2025-05-03，
  
  录用：2025-06-04，
  
  纸质出版：2025-11-20
- 稿件说明：
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习梦婷,王裕旗,王安义.基于特征值分解的多平台合成孔径定位方法[J].电信科学,2025,41(11):131-141.

XI Mengting,WANG Yuqi,WANG Anyi.An eigenvalue decomposition based multi-platform synthetic aperture positioning method[J].Telecommunications Science,2025,41(11):131-141.
习梦婷,王裕旗,王安义.基于特征值分解的多平台合成孔径定位方法[J].电信科学,2025,41(11):131-141. DOI： 10.11959/j.issn.1000-0801.2025206.

XI Mengting,WANG Yuqi,WANG Anyi.An eigenvalue decomposition based multi-platform synthetic aperture positioning method[J].Telecommunications Science,2025,41(11):131-141. DOI： 10.11959/j.issn.1000-0801.2025206.

摘要

针对单平台合成孔径定位（synthetic aperture positioning，SAP）方法在二维方向上分辨率不匹配和定位精度低的问题，提出一种基于特征值分解的多平台SAP方法。该方法建立了多站数据非相干累积定位的代价函数，利用特征值分解方法求解代价函数，以提高定位图像的局部信噪比和定位精度。为了进一步提高多平台SAP方法的计算效率，引入粒子群优化（particle swarm optimization，PSO）算法加速特征值分解的过程，以提升定位方法的计算效率。仿真结果表明，所提定位方法的均方根误差（root mean square error，RMSE）小于传统的两步定位方法和现有的多平台直接定位方法。引入改进的PSO算法后，定位时间大约缩短至改进前的1/20，计算效率有了明显的提升。

Abstract

A multi-platform synthetic aperture positioning (SAP) method based on eigenvalue decomposition was proposed to address the mismatched resolution and low positioning accuracy in two dimensions of single-platform SAP method. A cost function for non-coherent accumulation positioning from multi-station data was established. The cost function was solved using eigenvalue decomposition to improve the local signal-to-noise ratio and positioning accuracy of the positioning image. To further enhance the computational efficiency of the multi-platform SAP method

particle swarm optimization (PSO) algorithm was introduced to accelerate the eigenvalue decomposition process

thereby improving the computational efficiency of the positioning method. Simulation results show that the root mean square error (RMSE) of the proposed positioning algorithm is lower than that of traditional two-step positioning methods and existing direct multi-platform positioning methods. After the introduction of the improved PSO algorithm

the positioning time was approximately reduced to 1/20 of that before the improvement

significantly enhancing computational efficiency.

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references

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