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Discrete time nonlinear filters with informative observations are stable. (English) Zbl 1189.60133
Summary: The nonlinear filter associated with the discrete time signal-observation model \((X_k,Y_k)\) is known to forget its initial condition as \(k\to\infty\) regardless of the observation structure when the signal possesses sufficiently strong ergodic properties. Conversely, it stands to reason that if the observations are sufficiently informative, then the nonlinear filter should forget its initial condition regardless of any properties of the signal. We show that for observations of additive type \(Y_k=h(X_k)+\xi_k\) with invertible observation function \(h\) (under mild regularity assumptions on \(h\) and on the distribution of the noise \(\xi_k\)), the filter is indeed stable in a weak sense without any assumptions at all on the signal process. If the signal satisfies a uniform continuity assumption, weak stability can be strengthened to stability in total variation.

MSC:
60J05 Discrete-time Markov processes on general state spaces
62M20 Inference from stochastic processes and prediction
93E11 Filtering in stochastic control theory
93E15 Stochastic stability in control theory
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