Deep Kalman Filter

Deep Kalman filters (DKFs) combine the power of Kalman filtering for sequential data processing with the flexibility of deep learning models to represent complex, non-linear relationships. Current research focuses on improving the accuracy and robustness of DKFs through techniques like bidirectional filtering, importance sampling for tighter objective functions, and the development of continuous-time formulations for broader applicability. These advancements are leading to improved performance in diverse applications, including vehicle pose estimation, speech enhancement, and generative modeling of complex dynamical systems, demonstrating the DKF's growing significance in various fields.