Feedback Particle Filter

TL;DR

This paper introduces a novel feedback particle filter for nonlinear filtering, leveraging optimal control and mean-field game theory to improve posterior distribution matching, with practical algorithms and applications demonstrated.

Contribution

It presents a new feedback particle filter formulation based on optimal control and mean-field game theory, with an Euler-Lagrange equation and feedback structure for nonlinear filtering.

Findings

Exact posterior matching with identical priors

Numerical algorithms demonstrated in examples

Preliminary comparisons show advantages over bootstrap filter

Abstract

A new formulation of the particle filter for nonlinear filtering is presented, based on concepts from optimal control, and from the mean-field game theory. The optimal control is chosen so that the posterior distribution of a particle matches as closely as possible the posterior distribution of the true state given the observations. This is achieved by introducing a cost function, defined by the Kullback-Leibler (K-L) divergence between the actual posterior, and the posterior of any particle. The optimal control input is characterized by a certain Euler-Lagrange (E-L) equation, and is shown to admit an innovation error-based feedback structure. For diffusions with continuous observations, the value of the optimal control solution is ideal. The two posteriors match exactly, provided they are initialized with identical priors. The feedback particle filter is defined by a family of stochastic systems, each evolving under this optimal control law. A numerical algorithm is introduced and implemented in two general examples, and a neuroscience application involving coupled oscillators. Some preliminary numerical comparisons between the feed- back particle filter and the bootstrap particle filter are described.