Nonlinear Kalman Filter Toolbox

This text introduces a MATLAB Nonlinear Kalman Filter Toolbox that includes filter types such as UKF (Unscented Kalman Filter) and PF (Particle Filter). While described as highly practical, we can further explore its features and capabilities. Nonlinear Kalman filters are state estimation tools that compare model states with measurement results to estimate system states. The toolbox implements nonlinear Kalman filters through modular functions like ukf() and pf(), helping users better handle estimation problems in nonlinear systems. Both UKF and PF represent different types of nonlinear Kalman filters suitable for various systems and applications. The UKF implementation uses a set of deterministically chosen sigma points to approximate nonlinear transformations, while PF employs Monte Carlo methods with particle swarms to represent probability distributions. The toolbox provides configurable parameters for sigma point scaling (alpha, beta, kappa) in UKF and particle resampling techniques in PF. Additionally, it offers auxiliary functions for sensor calibration, fault detection through innovation analysis, and real-time performance monitoring using covariance matrices. Users can customize process models (f) and measurement models (h) through MATLAB function handles, making the toolbox adaptable to different nonlinear estimation scenarios.