Integrating GAN-Based Machine Learning with Nonlinear Kalman Filtering for Enhanced State Estimation

This study introduces a novel approach for enhancing state estimation in non-linear dynamic systems by integrating Generative Adversarial Networks (GANs) with the Unscented Kalman Filter (UKF). While the UKF improves upon traditional Kalman Filters by using sigma points to estimate the mean and covariance in non-linear transformations, its effectiveness is limited by static parameters - specifically, the process noise covariance (\(\:Q\)), measurement noise covariance (\(\:R\)), and scaling factors (primary, secondary, and tertiary; α, κ, and β). We propose a dynamic framework in which a GAN predicts and updates these parameters in real-time, based on the UKF’s recent performance, allowing the filter to better adapt to rapidly changing system dynamics. This method is validated on real-world aircraft navigation data containing time-stamped records of position, velocity, heading, and environmental variables. Results show that the GAN-enhanced UKF significantly reduces state estimation errors compared to conventional static models. The proposed framework is generalizable and can be applied to other domains such as robotics, autonomous vehicles, and smart cities, where accurate real-time state estimation under uncertainty is critical.