  This paper is concerned with the convergence and the error analysis for the
feedback particle filter (FPF) algorithm. The FPF is a controlled interacting
particle system where the control law is designed to solve the nonlinear
filtering problem. For the linear Gaussian case, certain simplifications arise
whereby the linear FPF reduces to one form of the ensemble Kalman filter. For
this and for the more general nonlinear non-Gaussian case, it has been an open
problem to relate the convergence and error properties of the finite-N
algorithm to the mean-field limit (where the exactness results have been
obtained). In this paper, the equations for empirical mean and covariance are
derived for the finite-N linear FPF. Remarkably, for a certain deterministic
form of FPF, the equations for mean and variance are identical to the Kalman
filter. This allows strong conclusions on convergence and error properties
based on the classical filter stability theory for the Kalman filter. It is
shown that the error converges to zero even with finite number of particles.
The paper also presents propagation of chaos estimates for the finite-N linear
filter. The error estimates are illustrated with numerical experiments.
