A Highly Maneuvering Target Tracking Algorithm Based on Continuous-Time Target Trajectory Function

To address the challenge of accurately tracking highly maneuvering targets in complex environments, this paper proposes a continuous-time target tracking algorithm based on the trajectory function of time (T-FoT). Unlike traditional discrete-time filtering methods that rely on predefined state-space models, the proposed approach represents target motion as a continuous function of time and position, and employs an incremental radial basis functions neural network (RBFNN) to approximate the trajectory in real time and obtain an explicit T-FoT expression. To enhance trajectory smoothness and stability, a second-order difference regularization term is introduced to constrain local curvature, and a dynamic basis-center adaptation mechanism is designed to improve long-horizon tracking performance. Furthermore, by integrating the recursive least squares (RLS) method into the RBFNN framework, the prediction uncertainty is explicitly quantified, enabling interpretable and reliable confidence assessment. Simulation results show that the proposed method achieves superior accuracy, smoothness, and robustness under strong maneuvering and noise conditions, and the estimated trajectory consistently remains within the 95% confidence interval, demonstrating strong anti-interference capability and reliable uncertainty modeling.