Robust limits of risk sensitive nonlinear filters

Deterministic filter models are considered, and a criterion for a determini stic filter to be robust is introduced. Among the candidates for robust det erministic filters are so-called minimax estimators. In the second part of the paper, a risk sensitive stochastic approach to nonlinear filtering is c onsidered, in which the traditional expected mean squared error criterion i s replaced by an expected exponential-of-mean squared error. Minimax filter s are obtained as totally risk averse limits of risk sensitive filters.