Penalized Maximum Likelihood Angular Super-Resolution Method for Scanning Radar Forward-Looking Imaging.

Area of Science:

• Radar imaging
• Signal processing
• Super-resolution techniques

Background:

• Deconvolution is crucial for angular super-resolution in scanning radar forward-looking imaging.
• Standard deconvolution methods are ill-posed, sensitive to noise, and prone to noise amplification with excessive iterations.

Purpose of the Study:

• To propose a penalized maximum likelihood angular super-resolution method to address noise sensitivity and amplification in radar imaging.
• To enhance the accuracy of noise modeling by considering independent I and Q channels.

Main Methods:

• A novel likelihood function is derived by analyzing noise in independent I and Q channels.
• A joint square-Laplace penalty is formulated, combining square constraint outlier sensitivity with Laplace distribution sparsity.
• An accelerated iterative strategy is employed to solve the resulting convex optimization problem.

Main Results:

• The proposed method demonstrates superior performance in angular super-resolution for radar imaging.
• Experiments with synthetic and real data validate the effectiveness of the penalized maximum likelihood approach.
• The method successfully mitigates noise amplification while preserving image resolving capabilities.

Conclusions:

• The developed penalized maximum likelihood method offers an effective solution for angular super-resolution in radar imaging.
• The novel penalty function and noise modeling enhance robustness against noise.
• The accelerated iterative solution facilitates practical engineering applications.