John Lataire

Learn More
In this paper, a nonparametric estimation procedure is presented in order to track the evolution of the dynamics of continuous (discrete)-time (non)-linear periodically time-varying (PTV) systems. Multisine excitations are applied to a PTV system since this kind of excitation signals allows us to discriminate between the noise and the nonlinear distortion(More)
This paper provides data-driven tools to detect and quantify approximately the influence of the time variation of a system under test in classical frequency response function (FRF) measurements. To achieve this, the best linear time-invariant approximation of a linear time-varying system is defined and is estimated using existing FRF estimators. An analysis(More)
Recently a method has been developed for detecting and quantifying the time-variation in frequency response function (FRF) measurements using arbitrary excitations [1]. The following basic assumptions have been made: (i) the input is known exactly (generalized output error stochastic framework), and (ii) the time-variant system operates in open loop. The(More)
The task of identifying an unknown dynamic system is made easier with prior knowledge on its behaviour. Using a frequency domain approach, the non-parametric maximum likelihood estimator of the system function, associated with the timedependent impulse response of a time-varying system, is constructed. This is accomplished by use of a simple linear least(More)
This paper presents a nonparametric method for detecting and quantifying the influence of time-variation in frequency response function (FRF) measurements. The method is based on the estimation of the best linear time-invariant (BLTI) approximation of a linear time-variant (LTV) system from known input, noisy output data. The key idea consists in(More)