One-dimensional Motion Representation Bernoulli Filter for Arc Motion Target Tracking

This paper proposes a novel approach to tracking targets moving along a circular arc trajectory using a onedimensional motion representation within the framework of the Bernoulli filter. The primary challenge in tracking such targets lies in accurately modeling their nonlinear motion dynamics while maintaining computational efficiency. Our method, Onedimensional Motion-Representation Bernoulli Filter (1DMR-BF), innovatively represents the complex two-dimensional circular motion as a one-dimensional problem by leveraging the inherent properties of circular trajectories, thereby significantly reducing the state space and computational load. The Bernoulli filter is employed to estimate the probability distribution of the target’s presence and its kinematic states simultaneously, enabling robust handling of uncertainties and potential detection failures. We derive the necessary mathematical formulations for the onedimensional motion model and integrate it with the Bernoulli filtering framework. Simulation results demonstrate that our proposed method outperforms traditional tracking algorithms in terms of accuracy and computational efficiency. This work contributes to the field of nonlinear target tracking by providing a computationally efficient and effective solution for arc motion targets.