Abstract
In this paper, we consider people counting and localization systems exploiting channel state information (CSI) measured from commodity WiFi network interface cards (NICs). CSI has useful information of amplitude and phase to describe signal propagation affected by the number of people or their locations in a designated space. However, due to hardware impairments of transceivers, CSI measurement suffers from offsets such as packet boundary detection uncertainty, sampling time difference, and carrier frequency difference. Moreover, an uncontrollable external environment where other WiFi devices communicate each other induces interfering signals, resulting in erroneous CSI captured at a receiver. In this paper, preprocessing of CSI is first proposed for offset removal, and it guarantees low-latency operation without any filtering process. The number of samples collected for each specific scenario is kept after packet-preserving preprocessing, which can be fully utilized to learn neural network models. Afterwards, we design people counting and localization models based on pre-training. To be adaptive to different measurement environments, meta-learning-based people counting and localization models are also proposed. We provide computational and space complexity analyses, confirming that the proposed meta-learning-based people counting and localization models require comparable resources to conventional adaptive models. Numerical results show that, compared with other learning-based benchmarks, the proposed scheme can achieve high sensing accuracy.
Main Figure
