[2510.27503] pDANSE: Particle-based Data-driven Nonlinear State Estimation from Nonlinear Measurements

Electrical Engineering and Systems Science > Signal Processing arXiv:2510.27503 (eess) [Submitted on 31 Oct 2025 (v1), last revised 3 Apr 2026 (this version, v2)] Title:pDANSE: Particle-based Data-driven Nonlinear State Estimation from Nonlinear Measurements Authors:Anubhab Ghosh, Yonina C. Eldar, Saikat Chatterjee View a PDF of the paper titled pDANSE: Particle-based Data-driven Nonlinear State Estimation from Nonlinear Measurements, by Anubhab Ghosh and 2 other authors View PDF Abstract: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. In the case of DANSE, the measurement system was linear, leading to a closed-form solution for the state posterior. However, the presence of a nonlinear measurement system renders a closed-form solution infeasible. Instead, the secondorder statistics of the state posterior are computed using the nonlinear measurements observed at the time point. We address the nonlinear measurements using a reparameterization trickbased particle sampling approach, and estimate the second-order statistics of the state posterior. The proposed method is referred to as parti...