A switching strategy for adaptive state estimation

This paper develops a switching strategy for adaptive state estimation in systems represented by nonlinear, stochastic, discrete-time state space models (SSMs). The developed strategy is motivated by the fact that there is no single Bayesian estimator that is guaranteed to perform optimally for a given nonlinear system and under all operating conditions. The proposed strategy considers a bank of plausible Bayesian estimators for adaptive state estimation, and then switches between them based on their performance. The performance of a Bayesian estimator is assessed using a performance measure derived from the posterior Cramér-Rao lower bound (PCRLB). It is shown that the switching strategy is stable, and yields estimates that are at least as good as any individual estimator in the bank. The efficacy of the switching strategy is illustrated on a practical simulation example.