Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems
This work addresses filtering in dynamical systems for researchers and practitioners, offering a novel approach but is incremental as it builds on existing implicit generative models.
The paper tackled the problem of inferring states in dynamical systems by relaxing the Gaussian and affine assumptions of traditional Gaussian filtering, using implicit generative models, and achieved a significant advantage over Gaussian filtering and nonlinear kernel-based methods.
Filtering is a general name for inferring the states of a dynamical system given observations. The most common filtering approach is Gaussian Filtering (GF) where the distribution of the inferred states is a Gaussian whose mean is an affine function of the observations. There are two restrictions in this model: Gaussianity and Affinity. We propose a model to relax both these assumptions based on recent advances in implicit generative models. Empirical results show that the proposed method gives a significant advantage over GF and nonlinear methods based on fixed nonlinear kernels.