PiGGO integrates a learned graph neural ODE as the continuous-time dynamics model within an extended Kalman filter to enable online virtual sensing and uncertainty-aware state estimation for nonlinear dynamic systems with unknown model form and sparse sensing.
Simon, Optimal state estimation: Kalman, H infinity, and nonlinear approaches, John Wiley & Sons
2 Pith papers cite this work. Polarity classification is still indexing.
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The paper surveys AI-aided sensor fusion and learning approaches to improve precision in AUV navigation where traditional signals fail.
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PiGGO: Physics-Guided Learnable Graph Kalman Filters for Virtual Sensing of Nonlinear Dynamic Structures under Uncertainty
PiGGO integrates a learned graph neural ODE as the continuous-time dynamics model within an extended Kalman filter to enable online virtual sensing and uncertainty-aware state estimation for nonlinear dynamic systems with unknown model form and sparse sensing.
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AI-Aided Advancements in Autonomous Underwater Vehicle Navigation
The paper surveys AI-aided sensor fusion and learning approaches to improve precision in AUV navigation where traditional signals fail.