Multichannel and Wide-Angle SAR Imaging Based on Compressed Sensing

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.