Unscented Kalman Filter for INS Navigation and Positioning

This example presents the Unscented Kalman Filter (UKF) algorithm and its application to position and attitude estimation in inertial navigation systems. As a nonlinear filtering technique, UKF is widely employed across various domains including navigation, control systems, and signal processing. The case study demonstrates implementation approaches for integrating UKF with INS navigation, including sigma point selection, nonlinear transformation handling, and covariance update mechanisms. Key algorithmic components involve calculating sigma points through deterministic sampling, propagating them through nonlinear system models, and computing weighted mean/covariance for state estimation. We analyze the algorithm's advantages in handling nonlinearities without linearization errors, while addressing computational complexity challenges and potential enhancements like adaptive tuning or square-root implementations. Through this technical exploration, readers will grasp UKF's fundamental principles and practical implementation strategies for real-world engineering problems.