  In this paper, a novel feedback control-based particle filter algorithm for
the continuous-time stochastic hybrid system estimation problem is presented.
This particle filter is referred to as the interacting multiple model-feedback
particle filter (IMM-FPF), and is based on the recently developed feedback
particle filter. The IMM-FPF is comprised of a series of parallel FPFs, one for
each discrete mode, and an exact filter recursion for the mode association
probability. The proposed IMM-FPF represents a generalization of the
Kalmanfilter based IMM algorithm to the general nonlinear filtering problem.
  The remarkable conclusion of this paper is that the IMM-FPF algorithm retains
the innovation error-based feedback structure even for the nonlinear problem.
The interaction/merging process is also handled via a control-based approach.
The theoretical results are illustrated with the aid of a numerical example
problem for a maneuvering target tracking application.
