FPPF uses a learned conditional generative proposal approximating the optimal proposal in particle filters, with tractable likelihoods for Bayesian updates and localization for high dimensions, outperforming baselines on nonlinear non-Gaussian systems.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
PA-PINPF adds Deep Sets population encoders (state or feature) to PINPF for better Bayesian posterior particle transport on range-measurement and TDOA tasks.
Filtering algorithms reconstruct trajectories of in-silico particles in a stirred tank reactor from noisy IMU data with errors below 10%.
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Tracking in-silico Lagrangian sensors in a lab-scale stirred tank reactor
Filtering algorithms reconstruct trajectories of in-silico particles in a stirred tank reactor from noisy IMU data with errors below 10%.