Joint compressed sensing imaging and phase adjustment via an iterative method for multistatic passive radar

Jue WANG; Jun WANG

doi:10.1631/FITEE.1601423

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Joint compressed sensing imaging and phase adjustment via an iterative method for multistatic passive radar

Regular Papers | Updated：2022-05-19

- Joint compressed sensing imaging and phase adjustment via an iterative method for multistatic passive radar
- 基于迭代方法的联合压缩感知与相位修正多站外辐射源雷达成像算法研究
- Frontiers of Information Technology & Electronic Engineering Vol. 19, Issue 4, Pages: 557-568(2018)
- Affiliations：
  
  National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China
- Author bio：
  
  Jue WANG, E-mail: xdwangjue@163.com
- Funds：
- DOI：10.1631/FITEE.1601423
  CLC： TN95
- Received：13 September 2016，
  
  Revised：2018-;4-12，
  
  Published：2018-04
- Accepted：
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Jue WANG, Jun WANG. Joint compressed sensing imaging and phase adjustment via an iterative method for multistatic passive radar[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(4): 557-568.
DOI：

Jue WANG, Jun WANG. Joint compressed sensing imaging and phase adjustment via an iterative method for multistatic passive radar[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(4): 557-568. DOI： 10.1631/FITEE.1601423.

摘要

受限于机会照射源信号通常为窄带信号，多站外辐射源雷达成像系统分辨率通常较低。另外，由于跟踪系统精度影响及站址测量过程中存在测量误差，系统成像质量会严重下降。为提高多站外辐射源雷达成像系统成像性能，开展了基于层析成像原理的高分辨外辐射源雷达成像算法研究。进一步，将压缩感知技术应用于外辐射源雷达成像系统中，以改善系统成像分辨力。最后，针对非合作平台测量误差引入的相位误差，提出相应的外辐射源雷达成像系统相位补偿方法。通过最小化图像的重构误差建立了相位误差求解模型，基于定点迭代技术对相位误差模型求解。所提相位误差补偿技术是一种非参数化方法，可用于补偿任意形式相位误差。理论分析与仿真实验验证了所提方法的有效性和收敛性。

Abstract

The resolution of the multistatic passive radar imaging system (MPRIS) is poor due to the narrow bandwidth of the signal transmitted by illuminators of opportunity. Moreover

the inaccuracies caused by the inaccurate tracking system or the error position measurement of illuminators or receivers can deteriorate the quality of an image. To improve the performance of an MPRIS

an imaging method based on the tomographic imaging principle is presented. Then the compressed sensing technique is extended to the MPRIS to realize high-resolution imaging. Furthermore

a phase correction technique is developed for compensating for phase errors in an MPRIS. Phase errors can be estimated by iteratively solving an equation that is derived by minimizing the mean recovery error of the reconstructed image based on the principle of fixed-point iteration technique. The technique is nonparametric and can be used to estimate phase errors of any form. The effectiveness and convergence of the technique are confirmed by numerical simulations.

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