# On Time-Reversal Imaging by Statistical Testing

## TL;DR

This paper develops adaptive wideband time-reversal imaging algorithms based on statistical hypothesis testing, enabling robust detection of single scatterers in noisy environments through cell-by-cell processing.

## Contribution

It introduces theoretically-founded decision statistics for hypothesis testing in time-reversal imaging, validated against maximal invariant statistics for improved robustness.

## Key findings

- Algorithms adapt to noise levels across frequencies
- Decision statistics effectively detect single scatterers
- Validation shows theoretical soundness and practical robustness

## Abstract

This letter is focused on the design and analysis of computational wideband time-reversal imaging algorithms, designed to be adaptive with respect to the noise levels pertaining to the frequencies being employed for scene probing. These algorithms are based on the concept of cell-by-cell processing and are obtained as theoretically-founded decision statistics for testing the hypothesis of single-scatterer presence (absence) at a specific location. These statistics are also validated in comparison with the maximal invariant statistic for the proposed problem.