Robust Adaptive Kalman Filter Algorithm for GPS Navigation

This article explores the robust adaptive Kalman filter algorithm used in Global Positioning System (GPS) navigation and provides MATLAB source code. The Kalman filter algorithm is a mathematical method for estimating system states that calculates optimal state estimates based on prior system states and measurement values. The robust adaptive Kalman filter algorithm represents an enhanced version of the standard Kalman filter that effectively reduces the impact of measurement errors on state estimation. In this article, we thoroughly examine the principles and applications of the robust adaptive Kalman filter algorithm, accompanied by MATLAB source code for reference and implementation purposes. The MATLAB implementation includes key functions for handling measurement outliers and adapting process noise covariance, ensuring robust performance in real-world GPS navigation scenarios. We hope this article helps readers gain a better understanding of the robust adaptive Kalman filter algorithm in GPS navigation systems.