Integrating GAN-based machine learning with nonlinear Kalman filtering for enhanced state estimation

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Signal Processing

Background:

• Unscented Kalman Filter (UKF) offers improved state estimation for non-linear systems over traditional Kalman Filters.
• UKF performance is constrained by static parameters: process noise covariance (Q), measurement noise covariance (R), and scaling factors (α, κ, β).
• Adaptability to changing system dynamics is crucial for accurate real-time state estimation.

Purpose of the Study:

• To develop a novel framework enhancing state estimation in non-linear dynamic systems.
• To dynamically adapt UKF parameters in real-time using Generative Adversarial Networks (GANs).
• To improve the accuracy and robustness of state estimation in complex, changing environments.

Main Methods:

• Integration of Generative Adversarial Networks (GANs) with the Unscented Kalman Filter (UKF).
• Real-time prediction and updating of UKF static parameters (Q, R, α, κ, β) by a GAN.
• Validation using real-world aircraft navigation data (position, velocity, heading, environmental variables).

Main Results:

• The GAN-enhanced UKF demonstrated significant reduction in state estimation errors compared to static models.
• The dynamic parameter adjustment enabled better adaptation to changing system dynamics.
• Improved accuracy in estimating aircraft navigation states.

Conclusions:

• The proposed GAN-UKF framework offers a significant advancement in state estimation for non-linear dynamic systems.
• Dynamic parameter adaptation is key to improving filter performance in uncertain and changing environments.
• The framework is generalizable to other critical domains like robotics, autonomous vehicles, and smart cities.