Target Tracking Using Extended Kalman Filter Algorithm for Maneuvering Motion

A maneuvering target model is proposed with three motion phases: initial uniform linear motion, uniform circular motion, and return to uniform linear motion, while maintaining constant linear velocity v. The Extended Kalman Filter (EKF) algorithm is implemented for localization and tracking, with simulation results demonstrating that this model aligns with practical maneuvering characteristics while facilitating mathematical processing. The filtering algorithm exhibits stable performance with fast convergence speed and high positioning accuracy, significantly improving tracking precision for maneuvering targets and enhancing system real-time capabilities.

Uniform Linear Motion and Uniform Circular Motion Models

This program simulates a maneuvering target undergoing three distinct phases: initial uniform linear motion, uniform circular motion, and a return to uniform linear motion. Throughout these phases, the linear velocity magnitude v remains constant. The simulation includes physical force implementations to model trajectory deviations and centripetal force effects.