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Efficient Parameter Estimation for Sparse SAR Imaging Based on Complex Image and Azimuth-Range Decouple.

Mingqian Liu^1,2,3, Bingchen Zhang^4,5,6, Zhongqiu Xu^7,8,9

¹Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China. liumingqian171@mails.ucas.ac.cn.

Sensors (Basel, Switzerland)

|October 23, 2019

Summary

This summary is machine-generated.

Estimating sparsity is crucial for sparse Synthetic Aperture Radar (SAR) imaging. This study proposes an efficient adaptive method to estimate sparsity, significantly reducing computational costs for large-scale SAR imaging applications.

Keywords:

Gaofen-3 data L1 regularization adaptive parameter estimation azimuth-range decouple compressive sensing (CS)sparse synthetic aperture radar (SAR) imaging

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Area of Science:

Signal Processing
Remote Sensing
Computational Imaging

Background:

Sparse signal processing is vital for Synthetic Aperture Radar (SAR) imaging.
Accurate sparsity estimation is critical but challenging in practical SAR applications.
Existing methods for regularization parameter estimation in sparse SAR imaging incur high computational and memory costs.

Purpose of the Study:

To propose an efficient adaptive parameter estimation method for sparse SAR imaging.
To reduce the computational complexity associated with sparsity estimation.
To enable efficient sparse SAR imaging for large-scale scenes.

Main Methods:

A novel complex image-based sparse SAR imaging approach is introduced.
Parameter pre-estimation is performed on the complex image by considering threshold operations.
Adaptive parameter estimation is then conducted in the raw data domain using pre-estimated parameters and azimuth-range decouple operators.

Main Results:

The proposed method significantly reduces computational complexity from quadratic to linear logarithmic order.
The method effectively estimates sparsity in sparse SAR imaging.
Validation is demonstrated using simulated and Gaofen-3 SAR data.

Conclusions:

The developed adaptive parameter estimation method enhances computational efficiency in sparse SAR imaging.
This approach is suitable for processing large-scale SAR scenes.
The findings validate the effectiveness and efficiency of the proposed technique for sparse SAR imaging.