Optimality of CUSUM Rule Approximations in Change-Point Detection Problems : Application to Nonlinear State-Space Systems

Abstract : The well-known cumulative sum (CUSUM) sequential rule for abrupt model change detection in stochastic dynamic systems relies on the knowledge of the probability density functions of the system output variables conditional on their past values and on the system functioning mode at each time step. This paper shows how to build an asymptotically optimal detection rule under the common average run length (ARL) constraint when these densities are not available but can be consistently estimated. This is the case for nonlinear state-space systems observed through output variables: for such systems, a new class of particle filters based on convolution kernels allows to get consistent estimates of the conditional densities, leading to an optimal CUSUM-like filter detection rule (FDR).
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IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2008, 54 (11), pp.5102-5112. 〈10.1109/tit.2008.929964〉
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Soumis le : mercredi 4 septembre 2013 - 10:02:57
Dernière modification le : jeudi 20 juillet 2017 - 16:33:39

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Ghislain Verdier, Nadine Hilgert, Jean-Pierre Vila. Optimality of CUSUM Rule Approximations in Change-Point Detection Problems : Application to Nonlinear State-Space Systems. IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2008, 54 (11), pp.5102-5112. 〈10.1109/tit.2008.929964〉. 〈hal-00857814〉

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