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Sparse Auto-Calibration for Radar Coincidence Imaging with Gain-Phase Errors.

Xiaoli Zhou1, Hongqiang Wang2, Yongqiang Cheng3

  • 1School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China. zhouxiaoli@nudt.edu.cn.

Sensors (Basel, Switzerland)
|November 4, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a sparse auto-calibration method to fix gain-phase errors in radar coincidence imaging (RCI). The new technique improves image quality and accurately estimates errors during the imaging process.

Keywords:
auto-calibrationgain-phase errororthogonal matching pursuit (OMP)radar coincidence imaging (RCI)sparse recovery

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Area of Science:

  • Signal Processing
  • Radar Imaging
  • Electromagnetics

Background:

  • Radar coincidence imaging (RCI) is a high-resolution staring technique.
  • Sparsity-driven methods are common in RCI but require accurate imaging models.
  • Gain-phase error is a significant model error that degrades RCI quality.

Purpose of the Study:

  • To propose a sparse auto-calibration method for compensating gain-phase error in RCI.
  • To enable simultaneous estimation of target and gain-phase error within the imaging process.

Main Methods:

  • An iterative algorithm combining target reconstruction and gain-phase error estimation.
  • Utilized orthogonal matching pursuit (OMP) for target reconstruction.
  • Employed Newton's method for gain-phase error estimation.

Main Results:

  • The proposed method significantly improves RCI imaging quality.
  • Accurate estimation of gain-phase error was achieved.
  • Simulation results validated the effectiveness of the auto-calibration approach.

Conclusions:

  • The sparse auto-calibration method effectively compensates for gain-phase errors in RCI.
  • This approach enhances the robustness and accuracy of radar imaging systems.
  • The method offers a practical solution for improving RCI performance in the presence of model uncertainties.