Passive Human Localization with the Aid of Reconfigurable Intelligent Surface

The past years have witnessed increasing research interests in achieving passive human localization using WiFi signals. However, due to the limited spatial resolution of WiFi devices, it is still difficult to achieve accurate localization with existing WiFi infrastructures. To tackle this problem, in this paper, we propose a RIS-aided passive localization framework, which exploits the degree of freedom provided by the Reconfigurable Intelligent Surface (RIS) to achieve accurate localization. We have noted that RIS is composed of a large number of controllable reflective elements, which can break through the resolution limitation of commodity WiFi devices without making any changes to existing infrastructures. Hence, we propose a phase control optimization algorithm that can maximize the discrepancy between human reflection and multi-path interference. In order to solve the near-far effect in multi-person scenario, we propose a Side-lobe Cancellation Algorithm to separate the reflected signals of different people and achieve accurate localization. Extensive simulation results demonstrate the proposed framework is capable of locating the moving persons passively with sub-centimeter accuracy in the presence of noise and multi-path interference.