Kalman filter based MIMO CSI phase recovery for COTS WiFi devices

TL;DR

This paper introduces an adaptive Kalman filter-based method for accurately recovering MIMO WiFi CSI phases distorted by hardware impairments, enhancing wireless sensing applications.

Contribution

It presents a novel Kalman filter with MAP estimation that models hardware-induced phase distortions for improved CSI phase recovery.

Findings

The proposed method accurately tracks channel variations.

It effectively eliminates phase errors caused by hardware impairments.

Performance approaches the Cramer Rao lower bound.

Abstract

Recently channel state information (CSI) measurements from commercial multi input multi output (MIMO) WiFi systems have been ubiquitously used for different wireless sensing applications. However, the phase of the CSI realizations is usually distorted severely by phase errors due to the hardware impairments, which significantly reduce the sensing performance. In this paper, we directly utilize the modeling of the phase distortions caused by the hardware impairments and propose an adaptive CSI estimation approach based on Kalman filter (KF) with maximum a posteriori (MAP) estimation that considers the CSI from the previous time. The performance of the proposed algorithm is compared against the Cramer Rao lower bound (CRLB). Simulation and experimental results demonstrate that our approach can track the channel variations while eliminating the phase errors accurately.