Estimating the Effective Permittivity for Reconstructing Accurate Microwave-Radar Images

Area of Science:

• Electromagnetics and Imaging Science
• Biomedical Engineering
• Computational Mathematics

Background:

• Microwave-radar imaging requires accurate dielectric property estimation for optimal image reconstruction.
• Current methods may lack efficiency or precision in determining these properties.
• The Tissue Sensing Adaptive Radar (TSAR) system necessitates robust algorithms for effective operation.

Purpose of the Study:

• To develop and present a novel method for estimating the optimal effective permittivity for microwave-radar image reconstruction.
• To define a quantitative measure of image quality and use it to guide permittivity estimation.
• To validate the proposed method using simulated, experimental, and patient data.

Main Methods:

• Defining image quality through a fitness function based on image formation characteristics.
• Constructing a polynomial interpolant of the fitness function to identify optimal permittivity values.
• Employing a novel adaptive sampling strategy combining stochastic collocation and polynomial chaos for computational efficiency.

Main Results:

• Demonstrated accurate microwave-radar image reconstruction using the proposed permittivity estimation method.
• Showcased the method's effectiveness across simulated, experimental, and patient datasets.
• Validated the ability to achieve accurate reconstructions from broad initial permittivity estimates.

Conclusions:

• The presented method provides an efficient and accurate approach to estimating optimal effective permittivity for microwave-radar imaging.
• This technique enhances the quality of reconstructed images, particularly when initial dielectric property information is limited.
• The findings support the further development and application of the Tissue Sensing Adaptive Radar system.