Meta-Learning-Based People Counting and Localization Models Employing CSI from Commodity WiFi NICs
For WiFi-based sensing applications, this work addresses the challenge of adapting models to different environments, but the improvement is incremental over existing methods.
The paper proposes meta-learning-based people counting and localization models using CSI from commodity WiFi NICs, achieving high sensing accuracy compared to standard training-test schemes.
In this paper, we consider people counting and localization systems exploiting channel state information (CSI) measured from commodity WiFi network interface cards (NICs). While CSI has useful information of amplitude and phase to describe signal propagation in a measurement environment of interest, CSI measurement suffers from offsets due to various uncertainties. 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. 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. Numerical results show that the proposed meta-learning-based people counting and localization models can achieve high sensing accuracy, compared to other learning schemes that follow simple training and test procedures.