Unscented Particle Filter Toolbox

In this technical article, we provide an in-depth exploration of the highly effective Unscented Particle Filter (UPF) methodology for tracking estimation. This sophisticated approach extends beyond basic position tracking to comprehensively estimate velocity, acceleration, orientation, and multiple dynamic parameters. To facilitate practical understanding, our toolbox includes ready-to-use implementations of Particle Filter (PF), Kalman Filter (KF), and Unscented Kalman Filter (UKF) algorithms. These implementations feature key functions for state prediction, measurement updates, and resampling operations, allowing researchers to directly experiment with different filtering techniques. The codebase demonstrates optimal particle propagation using unscented transformations and effective importance sampling strategies. This comprehensive resource serves as both an educational framework and practical toolkit, enabling users to efficiently learn and apply UPF technology to complex tracking scenarios across various domains.