用卡尔曼滤波估测电动转向系统中的高频干扰扭矩，仅靠电机数据实现。

Steer-by-Wire systems replace mechanical linkages, which provide benefits like weight reduction, design flexibility, and compatibility with autonomous driving. However, they are susceptible to high-frequency disturbances from unintentional driver torque, known as driver impedance, which can degrade steering performance. Existing approaches either rely on direct torque sensors, which are costly and impractical, or lack the temporal resolution to capture rapid, high-frequency driver-induced disturbances. We address this limitation by designing a Kalman filter-based disturbance observer that estimates high-frequency driver torque using only motor state measurements. We model the drivers passive torque as an extended state using a PT1-lag approximation and integrate it into both linear and nonlinear Steer-by-Wire system models. In this paper, we present the design, implementation and simulation of this disturbance observer with an evaluation of different Kalman filter variants. Our findings indicate that the proposed disturbance observer accurately reconstructs driver-induced disturbances with only minimal delay 14ms. We show that a nonlinear extended Kalman Filter outperforms its linear counterpart in handling frictional nonlinearities, improving estimation during transitions from static to dynamic friction. Given the study's methodology, it was unavoidable to rely on simulation-based validation rather than real-world experimentation. Further studies are needed to investigate the robustness of the observers under real-world driving conditions.