Meta-Learning-Based People Counting and Localization Models Employing CSI from Commodity WiFi NICs

TL;DR

This paper introduces meta-learning models for accurate people counting and localization using CSI from commodity WiFi NICs, addressing measurement uncertainties and environmental interference.

Contribution

It proposes preprocessing for CSI offset removal and develops adaptive meta-learning models that outperform traditional training methods.

Findings

Meta-learning models achieve higher sensing accuracy.

Preprocessing guarantees low-latency operation without filtering.

Models adapt well to different measurement environments.

Abstract

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.