Feedback particle filter with mean-field coupling
Tao Yang, Prashant G. Mehta, Sean Meyn · 2011
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 resulting control system is called the feedback particle filter. An algorithm is introduced and implemented in two numerical examples. A numerical comparison of the feedback particle filter with the bootstrap particle filter is provided.