Differentiable Particle Filter

Differentiable particle filters (DPFs) combine the power of sequential Monte Carlo methods for state estimation with the flexibility of neural networks for model learning in complex, non-linear systems. Current research focuses on improving DPF efficiency and accuracy through advancements in gradient estimation, resampling techniques (e.g., optimal placement), and the incorporation of more expressive model architectures like normalizing flows and regime-switching models. This approach enables data-adaptive inference, particularly valuable in scenarios with limited or noisy data, and finds applications in diverse fields such as robotics (e.g., object tracking, localization), and graph representation learning, offering significant improvements over traditional methods.