Comparison among the variants of subspace-based optimization method for addressing inverse scattering problems:...

Area of Science:

• Computational physics
• Applied mathematics
• Signal processing

Background:

• The inverse scattering problem is crucial in various scientific and engineering fields.
• Subspace-based optimization methods (SOM) offer an efficient approach to solving these problems.
• Algorithm performance is often limited by the determination of an ambiguous portion.

Purpose of the Study:

• To conduct a comparative analysis of different subspace-based optimization method (SOM) variants.
• To identify the optimal SOM for accurately determining the ambiguous portion in inverse scattering problems.
• To assess the impact of the ambiguous portion on computational cost and reconstruction capability.

Main Methods:

• Numerical experiments were designed to evaluate the performance of SOM variants.
• Comparative analysis focused on the efficiency and accuracy of each variant in determining the ambiguous portion.
• Performance metrics included computational cost and the quality of the reconstructed solution.

Main Results:

• Specific SOM variants demonstrated superior performance in determining the ambiguous portion.
• The choice of SOM variant significantly influenced both computational expense and reconstruction fidelity.
• The accurate determination of the ambiguous portion is key to enhancing algorithm capabilities.

Conclusions:

• The optimal subspace-based optimization method (SOM) variant was identified for inverse scattering problems.
• Findings provide guidance for selecting efficient algorithms in computational imaging and related fields.
• This research contributes to improving the accuracy and efficiency of inverse problem solutions.