  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.
