  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. This letter
addresses 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. To enhance the models' adaptability, particularly in the
presence of noise, data augmentation techniques are applied. 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.
Through the devised approach, two types of subwavelength entities are
successfully identified and precisely localized through numerical simulations
and experimental verification in laboratory environment. 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).
