Nonlinear Estimation Toolbox

This toolbox provides robust implementations of various nonlinear estimation algorithms such as particle filtering, unscented Kalman filtering (UKF), and extended Kalman filtering (EKF). These algorithms incorporate probabilistic state estimation techniques to enhance the accuracy of system state and output predictions. The particle filter implementation uses sequential Monte Carlo methods for handling non-Gaussian distributions, while UKF employs sigma-point transformation to avoid Jacobian calculations and EKF utilizes first-order linearization for nonlinear systems. Additionally, the toolbox includes simulation and analysis utilities featuring MATLAB-compatible functions for data visualization, parameter tuning, and performance validation. These integrated tools help users better understand their data patterns and optimize algorithm parameters through practical examples and modular code structure. By leveraging this toolbox, users can significantly improve their data analysis and prediction capabilities, making research and engineering workflows more efficient and accurate.