English

Quantitative Particle Approximation for Controlled Nonlinear Filtering

Optimization and Control 2026-08-01 v1

Abstract

We estimate convergence rates of value functions for particle approximations of a controlled nonlinear filtering problem. The state is a McKean--Vlasov diffusion on the flat torus, driven by hidden idiosyncratic noise and observed common noise. The filter---the conditional law of the state given the observations---serves as the state variable of the control problem, and the associated value function solves a second-order Hamilton--Jacobi--Bellman equation on the Wasserstein space. We approximate this problem by a centralized NN-particle control problem with independent idiosyncratic noises and a common observation noise. The framework accommodates nonseparable rewards and controlled drifts. Since a single control is applied to the entire population, the Hamiltonian is defined by an optimization performed after integration over the population. Under smoothness of the data, uniform ellipticity, and regularity of this Hamiltonian, we establish uniform value-function error bounds of order N1/6N^{-1/6} for d=1d=1, N1/6(logN)1/3N^{-1/6}(\log N)^{1/3} for d=2d=2, and N1/(3d)N^{-1/(3d)} for d>2d>2. The proof combines a translation lift in the common-noise direction, Fourier--Wasserstein inf- and sup-convolutions, viscosity comparison, and particle derivative estimates uniform in NN.

Cite

@article{arxiv.2608.00686,
  title  = {Quantitative Particle Approximation for Controlled Nonlinear Filtering},
  author = {Erhan Bayraktar and Ibrahim Ekren and Xihao He and Xin Zhang},
  journal= {arXiv preprint arXiv:2608.00686},
  year   = {2026}
}