An eigenvalue decomposition based multi-platform synthetic aperture positioning method

XI Mengting; WANG Yuqi; WANG Anyi

doi:10.11959/j.issn.1000-0801.2025206

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An eigenvalue decomposition based multi-platform synthetic aperture positioning method

Research and Development | 更新时间：2026-01-20

- An eigenvalue decomposition based multi-platform synthetic aperture positioning method
- Telecommunications Science Vol. 41, Issue 11, Pages: 131-141(2025)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(62401459;62222113)
- DOI：10.11959/j.issn.1000-0801.2025206
  CLC： TN911.7
- Received：04 March 2025，
  
  Revised：2025-05-03，
  
  Accepted：04 June 2025，
  
  Published：20 November 2025
- 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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