WiFi-Based Channel Impulse Response Estimation and Localization via Multi-Band Splicing

TL;DR

This paper introduces a multi-band splicing technique for WiFi channel impulse response estimation that enhances localization accuracy by combining CSI data across multiple frequency bands and compensating for hardware-induced distortions.

Contribution

The authors develop a novel multi-band splicing method with a per-band distortion correction algorithm that improves CIR estimation without relying on restrictive assumptions.

Findings

Outperforms existing methods in localization accuracy

Effectively removes hardware-induced CSI distortions

Achieves high-resolution sparse CIR recovery

Abstract

Using commodity WiFi data for applications such as indoor localization, object identification and tracking and channel sounding has recently gained considerable attention. We study the problem of channel impulse response (CIR) estimation from commodity WiFi channel state information (CSI). The accuracy of a CIR estimation method in this setup is limited by both the available channel bandwidth as well as various CSI distortions induced by the underlying hardware. We propose a multi-band splicing method that increases channel bandwidth by combining CSI data across multiple frequency bands. In order to compensate for the CSI distortions, we develop a per-band processing algorithm that is able to estimate the distortion parameters and remove them to yield the "clean" CSI. This algorithm incorporates the atomic norm denoising sparse recovery method to exploit channel sparsity. Splicing clean CSI over M frequency bands, we use orthogonal matching pursuit (OMP) as an estimation method to recover the sparse CIR with high (M-fold) resolution. Unlike previous works in the literature, our method does not appeal to any limiting assumption on the CIR (other than the widely accepted sparsity assumption) or any ad hoc processing for distortion removal. We show, empirically, that the proposed method outperforms the state of the art in terms of localization accuracy.