  Accurately estimating the position of a patient's side robotic arm in real
time in a remote surgery task is a significant challenge, particularly in
Tactile Internet (TI) environments. This paper presents a Kalman Filter (KF)
based computationally efficient position estimation method. The study also
assume no prior knowledge of the dynamic system model of the robotic arm
system. Instead, The JIGSAW dataset, which is a comprehensive collection of
robotic surgical data, and the Master Tool Manipulator's (MTM) input are
utilized to learn the system model using System Identification (SI) toolkit
available in Matlab. We further investigate the effectiveness of KF to
determine the position of the Patient Side Manipulator (PSM) under simulated
network conditions that include delays, jitter, and packet loss. These
conditions reflect the typical challenges encountered in real-world Tactile
Internet applications. The results of the study highlight KF's resilience and
effectiveness in achieving accurate state estimation despite network-induced
uncertainties with over 90\% estimation accuracy.
