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Despite the cheap availability of computing resources enabling faster MonteCarlo simulations, the potential benefits of particle filtering in revealingaccurate statistical information on the imprecisely known model parameters ormodeling errors of dynamical systems, based on limited time series data, havenot been quite realized. A major numerical bottleneck precipitating thisunder-performance, especially for higher dimensional systems, is theprogressive particle impoverishment owing to weight collapse and the aim of thecurrent work is to address this problem by replacing weight-based updatesthrough additive ones. Thus, in the context of nonlinear filtering problems, anovel additive particle update scheme, in its non-iterative and iterativeforms, is proposed based on manipulations of the innovation integral in thegoverning Kushner-Stratonovich equation. Numerical evidence for theidentification of nonlinear and large dimensional dynamical systems indicates asubstantively superior performance of the non- iterative version of the EnKSvis-\`a-vis most existing filters. The costlier iterative version, thoughconceptually elegant, mostly appears to effect a marginal improvement in thereconstruction accuracy over its non-iterative counterpart. Prominent in thereported numerical comparisons are variants of the Ensemble Kalman Filter(EnKF) that also use additive updates, albeit with many inherent limitations ofa Kalman filter.

An Ensemble Kushner-Stratonovich (EnKS) Nonlinear Filter: Additive Particle Updates in Non-Iterative and Iterative Forms