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  1. Home
  3. Multichannel And Wide-angle Sar Imaging Based On Compressed Sensing.
  1. Home
  3. Multichannel And Wide-angle Sar Imaging Based On Compressed Sensing.

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Multichannel and Wide-Angle SAR Imaging Based on Compressed Sensing.

Chao Sun1, Baoping Wang2, Yang Fang3

  • 1School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China. sunchao2013@mail.nwpu.edu.cn.

Sensors (Basel, Switzerland)
|February 7, 2017

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new compressed sensing (CS) method for synthetic aperture radar (SAR) imaging. The joint sparse recovery approach improves scatterer precision and preserves vital scattering information for target recognition.

Keywords:
compressed sensingjoint sparse recoverymultichannelsynthetic aperture radarwide-angle

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Area of Science:

  • Remote Sensing
  • Signal Processing
  • Data Science

Background:

  • Synthetic aperture radar (SAR) imaging benefits from compressed sensing (CS) theory.
  • Independent CS application to each channel neglects inter-channel information and can cause scatterer position inconsistencies.
  • This inconsistency complicates the extraction of scattering information.

Purpose of the Study:

  • To propose a novel CS-based method for joint sparse recovery of multichannel SAR images.
  • To enhance imaging performance and preserve scattering information by exploiting joint sparsity.
  • To improve scatterer recovery precision and ensure positional consistency across channels.

Main Methods:

  • Exploiting the joint sparsity of the signal ensemble from multiple SAR channels or sub-apertures.
  • Developing a novel CS-based method for joint sparse recovery of all channel images.
  • Employing a modified orthogonal matching pursuit algorithm to solve the joint sparse recovery problem.

    • Main Results:

    • The proposed method effectively improves the recovery precision of scatterers.
    • Valuable scattering information is preserved during the image formation process.
    • The method achieves better imaging performance with fewer measurements compared to independent CS processing.

    Conclusions:

    • The joint sparse recovery method enhances multichannel SAR imaging performance.
    • It effectively preserves scattering information crucial for target recognition.
    • The approach offers superior results over independent CS processing for SAR applications.