A Kushner-Stratonovich Monte Carlo Filter Applied to Nonlinear Dynamical System Identification
Abstract
A Monte Carlo filter, based on the idea of averaging over characteristics and fashioned after a particle-based time-discretized approximation to the Kushner-Stratonovich (KS) nonlinear filtering equation, is proposed. A key aspect of the new filter is the gain-like additive update, designed to approximate the innovation integral in the KS equation and implemented through an annealing-type iterative procedure, which is aimed at rendering the innovation (observation-prediction mismatch) for a given time-step to a zero-mean Brownian increment corresponding to the measurement noise. This may be contrasted with the weight- based multiplicative updates in most particle filters that are known to precipitate the numerical problem of weight collapse within a finite-ensemble setting. A study to estimate the a-priori error bounds in the proposed scheme is undertaken. The numerical evidence, presently gathered from the assessed performance of the proposed and a few other competing filters on a class of nonlinear dynamic system identification and target tracking problems, is suggestive of the remarkably improved convergence and accuracy of the new filter.
Cite
@article{arxiv.1302.3330,
title = {A Kushner-Stratonovich Monte Carlo Filter Applied to Nonlinear Dynamical System Identification},
author = {S Sarkar and S R Chowdhury and M Venugopal and R M Vasu and D Roy},
journal= {arXiv preprint arXiv:1302.3330},
year = {2015}
}
Comments
51 pages, 6 figures