Physics Assisted Deep Learning for Indoor Imaging using Phaseless Wi-Fi Measurements

TL;DR

This paper introduces a physics-assisted deep learning method for indoor RF imaging using phaseless Wi-Fi signals, capable of reconstructing large, high-permittivity objects with high accuracy, surpassing existing techniques.

Contribution

It presents the first inverse scattering deep learning framework that accurately images large, high-permittivity objects using phaseless Wi-Fi measurements, combining a Rytov model with a modified U-Net.

Findings

Reconstructs objects with permittivity up to 77 and size up to 40 wavelengths.

Outperforms existing methods that only handle permittivity up to 3 or 4.

Validated through simulations and real indoor Wi-Fi experiments.

Abstract

A physics assisted deep learning framework to perform accurate indoor imaging using phaseless Wi-Fi measurements is proposed. It is able to image objects that are large (compared to wavelength) and have high permittivity values, that existing radio frequency (RF) inverse scattering techniques find very challenging, making it suitable for indoor RF imaging. The technique utilizes a Rytov based inverse scattering model with a deep learning framework. The inverse scattering model is based on an extended Rytov approximation (xRA) that pre-reconstructs the RF measurements. Under strong scattering conditions, this pre-reconstruction is related to the actual permittivity profile by a non-linear function, which is learned by a modified U-Net model to obtain the permittivity profile of the object. Thus, our proposed approach not only reconstructs the shape of objects, but also estimates their permittivity values accurately. We demonstrate its imaging performance using simulations as well as experimental results in an actual indoor environment using 2.4 GHz Wi-Fi phaseless measurements. For incident wavelength $\lambda_0$, the proposed framework can reconstruct objects with relative permittivity as high as 77 and electrical size as large as $40 \lambda$, where $\lambda =\lambda_0/\sqrt{77}$. This is in contrast to existing phaseless imaging techniques which cannot reconstruct permittivity values beyond 3 or 4. Thus, our proposed method is the first inverse scattering-based deep learning framework which can image large scatterers with high permittivity and achieve accurate indoor RF imaging using phaseless Wi-Fi measurements.