MetaSense: Boosting RF Sensing Accuracy Using Dynamic Metasurface Antenna

Abstract

Conventional radio-frequency (RF) sensing systems rely on either frequency diversity or spatial diversity to ensure high sensing accuracy. Such reliance introduces several practical limitations that hinder the pervasive deployment of existing solutions. To circumvent this prevalent reliance, we present MetaSense, a system that leverages antenna pattern diversity for fine-grained RF sensing. MetaSense incorporates the dynamic metasurface antenna (DMA) and the auxiliary-assisted ensemble multimask learning (AEMML) framework in its design. The DMA is a novel type of antenna that can provide a diverse set of uncorrelated radiation patterns in a low-cost and low-complexity manner. The AEMML is a quality-aware learning framework that can dynamically assess and aggregate the heterogeneous channel measurements from different antenna patterns to ensure high sensing accuracy. It also incorporates a transfer learning model that allows it to generalize to new sensing conditions with few training instances required. We prototype MetaSense and demonstrate its effectiveness on a writing motion recognition task using a custom-designed 2-D DMA. The results show that MetaSense achieves 92% to 98% accuracy in classifying ten miniature writing motions, outperforming a nontunable antenna by 20% in all scenarios. Moreover, when deployed in new sensing positions where limited training instances are available, MetaSense requires as few as five training instances per class to achieve over 90% accuracy.