A Sparse Super-Resolution Imaging Approach for Array Scanning Radar in High-Resolution Ground Mapping

Area of Science:

• Radar Systems Engineering
• Signal Processing
• Remote Sensing

Background:

• Phased array scanning radars are vital for high-resolution airborne ground mapping.
• Spatial variations in antenna patterns at large scan angles degrade super-resolution performance.
• Existing methods struggle with hardware-induced limitations in wide-swath imaging.

Purpose of the Study:

• To develop a super-resolution deconvolution method robust to spatial antenna pattern variations.
• To enhance the performance of forward-looking phased array scanning radar systems.
• To improve terrain perception and ground mapping accuracy in airborne sensing.

Main Methods:

• Analysis of antenna pattern spatial variation causes.
• Derivation of a modified antenna convolution matrix for accurate scanning modeling.
• Formulation of a sparse objective function.
• Application of an alternating direction method of multipliers (ADMM) solver with reweighted strategy.

Main Results:

• Accurate modeling of the radar scanning process despite spatial variations.
• Achieved an approximate 4x increase in cross-range resolution.
• Demonstrated enhanced observation capabilities in the forward-looking area.

Conclusions:

• The proposed sparse super-resolution method effectively addresses hardware-induced limitations in phased array scanning radar.
• The method significantly improves resolution and observation capabilities for airborne sensing.
• This advancement is crucial for high-resolution ground mapping and terrain perception.