MATLAB Source Code for UKF, EKF, and IMM Algorithms

This document references MATLAB source codes and documentation for UKF, EKF, and IMM algorithms. While the original description is concise, users may seek deeper insights into these implementations. The UKF algorithm utilizes sigma points to handle nonlinear systems without linearization, providing superior accuracy compared to EKF. The EKF implementation employs first-order Taylor series approximation for state estimation in nonlinear systems. The IMM algorithm combines multiple motion models with Markov transitions for robust maneuvering target tracking. Key implementation features include: - UKF code implements the unscented transformation with configurable alpha, beta, and kappa parameters - EKF version includes Jacobian matrix calculations for state transition and measurement functions - IMM code contains model probability updates and interaction operations through mixing probabilities - All algorithms support customizable process noise and measurement noise configurations For practical applications, these codes are particularly valuable in: - Aerospace navigation systems (attitude estimation using UKF) - Automotive radar tracking (IMM for vehicle trajectory prediction) - Robotics localization (EKF for sensor fusion) Users can extend functionality by modifying measurement models or adding custom motion models. The code structure allows straightforward integration with Simulink for system simulation. Experimental suggestions include comparing estimation performance under different noise conditions and validating results with ground truth data sets.