AI RESEARCH
pDANSE: Particle-based Data-driven Nonlinear State Estimation from Nonlinear Measurements
arXiv CS.LG
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ArXi:2510.27503v2 Announce Type: replace-cross We consider the problem of designing a data-driven nonlinear state estimation (DANSE) method that uses (noisy) nonlinear measurements of a process whose underlying state transition model (STM) is unknown. Such a process is referred to as a model-free process. A recurrent neural network (RNN) provides parameters of a Gaussian prior that characterize the state of the model-free process, using all previous measurements at a given time point.