  A novel form of nonlinear stochastic filtering employing an annealing-type
iterative update scheme, aided by the introduction of an artificial diffusion
parameter and based on the Gaussian sum approximations of the prior and
posterior densities, is presented. The proposed Monte Carlo filter bank
conforms in structure to the parent nonlinear filtering (Kushner-Stratonovich)
equation, as reflected in the additive gain-based updates, and possesses
excellent mixing properties enabling better explorations of the phase space of
the state vector. The performance of the filter bank, presently assessed
against a few carefully chosen numerical examples, provide ample evidence of
its substantively improved performance in terms of filter convergence and
estimation accuracy vis-\`a-vis a few other competing filters especially in
higher dimensional dynamic system identification problems including cases that
may demand estimating relatively minor variations in the parameter values from
their reference states.
