Integrating GAN-based machine learning with nonlinear Kalman filtering for enhanced state estimation
Lior Tobaly, Eyal Yaniv, Zeev Zalevsky · 2025
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Description
A peer-reviewed research article from Europe PMC (2025). 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).
Catalog record
| CREATOR | Lior Tobaly, Eyal Yaniv, Zeev Zalevsky |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 127 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |