Kalman Filter Based MIMO CSI Phase Recovery for COTS Wifi Devices

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 Cramér–Rao lower bound (CRLB). Simulation and experimental results demonstrate that our approach can track the channel variations while eliminating the phase errors accurately.