Learning Differentiable Particle Filter on the Fly
Differentiable particle filters are an emerging class of sequential Bayesian inference techniques that use neural networks to construct components in state space models. Existing approaches are mostly based on offline supervised training strategies. This leads to the delay of the model deployment and the obtained filters are susceptible to distribution shift of test-time data. In this paper, we propose an online learning framework for differentiable particle filters so that model parameters can be updated as data arrive. The technical constraint is that there is no known ground truth state information in the online inference setting. We address this by adopting an unsupervised loss to construct the online model updating procedure, which involves a sequence of filtering operations for online maximum likelihood-based parameter estimation. We empirically evaluate the effectiveness of the proposed method, and compare it with supervised learning methods in simulation settings including a multivariate linear Gaussian state-space model and a simulated object tracking experiment.
Code (1)
Tasks
Bayesian InferenceObject Trackingparameter estimationSequential Bayesian InferenceState Space ModelsSimilar Papers 제목 키워드 기반
An overview of differentiable particle filters for data-adaptive sequential Bayesian inference
By approximating posterior distributions with weighted samples, particle filters (PFs) provide an efficient mechanism for solving non-linear sequential state estimation problems. While the effectiveness of particle filte…
Bayesian InferenceSequential Bayesian InferenceState EstimationDifferentiable Particle Filters through Conditional Normalizing Flow
Differentiable particle filters provide a flexible mechanism to adaptively train dynamic and measurement models by learning from observed data. However, most existing differentiable particle filters are within the bootst…
Visual TrackingNormalizing Flow-based Differentiable Particle Filters
Recently, there has been a surge of interest in incorporating neural networks into particle filters, e.g. differentiable particle filters, to perform joint sequential state estimation and model learning for non-linear no…
Density EstimationNormalising FlowsState EstimationState Space Models+1Towards Differentiable Resampling
Resampling is a key component of sample-based recursive state estimation in particle filters. Recent work explores differentiable particle filters for end-to-end learning. However, resampling remains a challenge in these…
State EstimationPyDPF: A Python Package for Differentiable Particle Filtering
State-space models (SSMs) are a widely used tool in time series analysis. In the complex systems that arise from real-world data, it is common to employ particle filtering (PF), an efficient Monte Carlo method for estima…
Time Series Analysis