Differentiable Kalman Filter

Differentiable Kalman filters (DKFs) are a class of algorithms that combine the strengths of Kalman filtering for state estimation with the flexibility of neural networks for learning complex system dynamics directly from data. Current research focuses on applying DKFs to diverse areas, including audio processing (e.g., using differentiable filters for audio synthesis and effect modeling), robotics (e.g., for state estimation in soft robots and visual-inertial odometry), and autonomous driving (e.g., for multi-sensor cooperative tracking). This approach allows for more robust and adaptable state estimation in challenging scenarios, improving accuracy and reliability in various applications while enabling end-to-end training of complex systems.