Simultaneous Localization and Recognition of Subwavelength Non-Cooperative Entities Based on SISO Time Reversal and Neural Networks

作     者：Wang, Yinchen Duan, Yu Ye, Yuqi Wang, Ren Li, Biao Jiang, Bin Liu, Xin Wang, Bing-Zhong 

作者机构：University of Electronic Science and Technology of China Institute of Applied Physics Chengdu611731 China No. 36 Research Institute of CETC Communication Information Security Control Laboratory Jiaxing314033 China 

出 版 物：《IEEE Sensors Journal》 (IEEE Sensors J.)

年 卷 期：2024年

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

主　　题：Multiple scattering 

摘      要：The simultaneous localization and recognition of subwavelength non-cooperative entities within complex multi-scattering environments using a simplified system continues to pose a substantial challenge. In this paper, 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 the simultaneous subwavelength recognition and localization through numerical simulations for metallic entities and experimental testing for metallic and non-metallic 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). © 2001-2012 IEEE.