Laurent Mevel

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We consider a hidden Markov model with multidimensional observations, and with misspecification, i.e. the assumed coefficients (transition probability matrix, and observation conditional densities) are possibly different from the true coefficients. Under mild assumptions on the coefficients of both the true and the assumed models, we prove that : (i) the(More)
We consider a hidden Markov model (HMM) with multidimensional observations, and where the coefficients (transition probability matrix, and observation conditional densities) depend on some unknown parameter. We study the asymptotic behaviour of two recursive estimators, the recursive maximum likelihood estimator (RMLE), and the recursive conditional least(More)
In Operational Modal Analysis, the modal parameters (natural frequencies, damping ratios and mode shapes), obtained from Stochastic Subspace Identification of structures, are subject to statistical uncertainty from ambient vibration measurements. It is hence neccessary to evaluate the confidence intervals of these obtained results. This paper will propose(More)
Flutter is a critical instability phenomenon for aircrafts. In previous investigations, the authors have proposed several online statistical subspace-based algorithms for flutter monitoring. Each algorithm monitors some stability criterion (damping, flutter margin...) w.r.t. a fixed reference flight point using the online Cusum test. The drawback of this(More)
In this paper we study “nonstationary consistency” of subspace methods for eigenstructure identification, i.e., the ability of subspace algorithms to converge to the true eigenstructure despite nonstationarities in the excitation and measurement noises. Note that such nonstationarities may result in having time-varying zeros for the underlying system, so(More)
In this paper a new recursive output-only identification algorithm is proposed based on stochastic realization, a classical covariance driven subspace identification technique. The recursive algorithm yields results that are in close agreement with those of its non-recursive counterpart. Furthermore, the relatively low complexity of the algorithm makes it(More)
The problem of structural model identification under both known and unknown input excitations, is addressed. In-flight data analysis is an important instance of that problem. Input/output and output-only eigenstructure identification methods are described and compared, within two classes of methods: subspace-based and prediction error. In particular,(More)