The goal of target tracking is to estimate target position, velocity, and acceleration in real time using position data. This paper introduces a novel target-tracking technique that uses adaptive input and state estimation (AISE) for real-time numerical differentiation to estimate velocity, acceleration, and jerk from position data. These estimates are used to model the target motion within the Frenet-Serret (FS) frame. By representing the model in SE(3), the position and velocity are estimated using the invariant extended Kalman filter (IEKF). The proposed method, called FS-IEKF-AISE, is illustrated by numerical examples and compared to prior techniques.
@article{arxiv.2501.04262,
title = {Target Tracking Using the Invariant Extended Kalman Filter with Numerical Differentiation for Estimating Curvature and Torsion},
author = {Shashank Verma and Dennis S. Bernstein},
journal= {arXiv preprint arXiv:2501.04262},
year = {2025}
}