472998 A Bayesian Filter Switching Strategy for Simultaneous State and Parameter Estimation
The choice of an efficient Bayesian filter for simultaneous state and parameter estimation in nonlinear stochastic systems is still an open problem. This is because there is no single tractable Bayesian filter that is guaranteed to provide a consistent performance for a given system under all operating conditions . A practitioner is thus left with no clear substitute for the optimal Bayesian filter.
This paper develops a filter switching strategy for simultaneous state and parameter estimation in systems represented by nonlinear, stochastic, discrete-time state space models (SSMs). The proposed strategy considers a bank of plausible Bayesian filters for simultaneous state and parameter estimation, and then switches between them based on their performance. The performance of a Bayesian filter is assessed using a performance measure derived from the posterior Cramer-Rao lower bound (PCRLB). The efficacy of the filter switching strategy is illustrated on a practical simulation example.
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