Kalman Filtering, Unscented Filtering, and Particle Filtering Utilities

This article introduces a suite of utilities designed for Kalman filtering, unscented Kalman filtering (UKF), particle filtering, and other related algorithms. While these implementations are already stable and computationally efficient, their performance can be further enhanced through additional features. For instance, adaptive parameter tuning can be integrated to improve the algorithms' adaptability to diverse datasets and environmental conditions. Additionally, visualization tools could be incorporated to help users better interpret output results through interactive plots and real-time filtering state displays. The codebase may also be extended with new filtering algorithms to address broader problem domains, such as implementing square-root filtering variants for improved numerical stability or adding Rao-Blackwellized particle filters for high-dimensional state estimation. Although existing utilities provide solid foundations, continuous improvement through feature enhancements remains essential for advancing their applicability.