Simultaneous Localization and Recognition of Subwavelength Noncooperative Entities Based on SISO Time Reversal and Neural Networks

The simultaneous localization and recognition of subwavelength noncooperative entities within complex multiscattering environments using a simplified system continues to pose a substantial challenge. In this article, we address this challenge by synergistically integrating time reversal time-frequency phase prints (TRTFPPs) and neural networks. Initially, a time reversal (TR) single-input single-output (SISO) framework is employed to generate TRTFPPs. Subsequently, neural networks are employed to comprehend the TRTFPPs. Specifically, a cascaded neural network structure is embraced, encompassing both a recognition neural network and distinct neural networks for localizing different entities. Using the devised method, we have achieved simultaneous subwavelength recognition and localization through numerical simulations for metallic entities and experimental testing for metallic and nonmetallic entities. The proposed methodology holds applicability across various electromagnetic systems, including but not limited to detection, imaging, human-computer interaction, and the Internet of Things (IoT).