  The joint communication and sensing (JCAS) technique has drawn great
attention due to its high spectrum efficiency by using the same transmit signal
for both communication and sensing. Exploiting the correlation between the
uplink (UL) channel and the sensing results, we propose a sensing-aided Kalman
filter (SAKF)-based channel state information (CSI) estimation method for UL
JCAS, which exploits the angle-of-arrival (AoA) estimation to improve the CSI
estimation accuracy. A Kalman filter (KF)-based CSI enhancement method is
proposed to refine the least-square CSI estimation by exploiting the estimated
AoA as the prior information. Simulation results show that the bit error rates
(BER) of UL communication using the proposed SAKF-based CSI estimation method
approach those using the minimum mean square error (MMSE) method, while at
significantly reduced complexity.
