Gyroscope-Free UE Angular Velocity and Attitude Estimation With Channel State Information Variation

Attitude and angular velocity estimation is essential for various applications of the user equipment (UE). In gyroscope-free scenarios, how to perform attitude and angular velocity estimation is a critical issue. In this letter, we propose a UE attitude and angular velocity estimation framework with the assistance of channel state information (CSI) variation. We first present a detailed proof of the existence of the mapping from CSI variation to angular velocity. Furthermore, we employ a neural network to learn this mapping, leveraging its capacity to model complex relationships. Then the estimated angular velocity is incorporated into the kinematic model of the gyroscope to derive the current attitude estimate of the UE. Simulation results further substantiate the significant potential of our method as a viable alternative to gyroscopes. Moreover, results also show that more propagation paths leads to better angular velocity and attitude estimation.