Unscented Particle Filter Algorithm Implementation

This package implements the Unscented Particle Filter (UPF) algorithm, a nonlinear filtering method based on Kalman filter principles that demonstrates improvements in both accuracy and computational efficiency compared to conventional particle filters. The key distinction lies in its use of the Unscented Kalman Filter (UKF) to approximate the proposal distribution function, resulting in greater precision and stability during state estimation. Technically, the algorithm maintains Gaussian distribution properties through nonlinear transformations of system state variables, enabling effective filtering for nonlinear systems. The implementation includes sigma point generation for capturing mean and covariance statistics, followed by weight updates using importance sampling with UKF-based proposals. Additionally, the package provides auxiliary features such as interactive parameter visualization tools that allow users to monitor and adjust algorithm parameters in real-time, facilitating better understanding and optimization of filter performance through practical experimentation.