Optimized Localized Mode Selection Technique in Computational Microwave Imaging

This study presents a technique for optimizing measurement mode selection in computational microwave imaging (CMI)-based systems. The primary goal of the presented method is to enhance the computational efficiency while maintaining the imaging quality. To this end, we outline a Regional Average Correlation Matrix (RACM) approach to evaluate the effectiveness of measurement modes based on an area-specific analysis of near-field distributions. Additionally, after performing RACM, we develop and optimize a Contribution Matrix Sorting (CMS) algorithm. Finally, we use the enhanced algorithm to identify the most informative measurement modes in a CMI-based system. The validity of this approach is confirmed through full-wave simulations conducted in CST Microwave Studio, revealing a high imaging quality even when the number of measurement modes is reduced by 77 %. This work achieves a significant advancement in practical CMI applications, providing a solution to computational efficiency-related challenges in CMI while maintaining highquality imaging results.