A Kushner-Stratonovich Monte Carlo filter applied to nonlinear dynamical system identification

作者：Sarkar S; Chowdhury S R; Venugopal M; Vasu R M; Roy D^*

来源：Physica D: Nonlinear Phenomena , 2014, 270: 46-59.

DOI：10.1016/j.physd.2013.12.007

• Kushner-Stratonovich equation
• Euler approximation
• Inner iterations
• Monte Carlo filters
• Error estimates
• Nonlinear system identification

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.

• 出版日期2014-3-1