pDANSE enables nonlinear state estimation for model-free processes by using RNN-parameterized Gaussian priors and reparameterization-based particle sampling to compute posterior second-order statistics from nonlinear measurements.
An overview of differentiable particle filters for data-adaptive sequential bayesian inference,
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pDANSE: Particle-based Data-driven Nonlinear State Estimation from Nonlinear Measurements
pDANSE enables nonlinear state estimation for model-free processes by using RNN-parameterized Gaussian priors and reparameterization-based particle sampling to compute posterior second-order statistics from nonlinear measurements.