System Identification Using Measurements Subject to Stochastic Time Jitter

Eng, Frida; Gustafsson, Fredrik

doi:10.3182/20050703-6-cz-1902.00198

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…In [13], a weighted least squares estimator is proposed to estimate the time base drift (delay) introduced by a high-frequency sampling oscilloscope. In [14], a continuous time model frequency domain maximum-likelihood approach is developed but was not evaluated on output error models.…”

Section: Introductionmentioning

confidence: 99%

Identification of Pharmacokinetics Models in the Presence of Timing Noise

Bastogne

Mézières-Wantz

Ramdani

et al. 2008

European Journal of Control

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The problem addressed in this paper deals with the parameter estimation of in vitro uptake kinetics of drugs into living cells in presence of timing noise. Effects of the timing noise on the bias and variance of the output error are explicitly determined. A bounded-error parameter estimation approach is proposed as a suited solution to handle this problem. Application results are presented which emphasize the effectiveness of the methodology in such an experimental framework. 1 .

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Section: Introductionmentioning

confidence: 99%

Identification of Pharmacokinetics Models in the Presence of Timing Noise

Bastogne

Mézières-Wantz

Ramdani

et al. 2008

European Journal of Control

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“…Previous publications include analysis of the stochastic frequency window, W (f ). Studies of the expected value are made in Gunnarsson et al (2004), and signal estimation in the frequency domain for non-uniformly sampled signals is investigated in Eng and Gustafsson (2005b).…”

Section: Mean and Covariance Of W (F )mentioning

confidence: 99%

Frequency Domain Analysis of Signals With Stochastic Sampling Times

Eng

Gunnarsson

Gustafsson

2008

IEEE Trans. Signal Process.

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In non-uniform sampling (NUS), signal amplitudes and time stamps are delivered in pairs. Several methods to compute an approximate Fourier transform (AFT) have appeared in literature, and their posterior properties in terms of alias suppression and leakage have been addressed. In this paper, the sampling times are assumed to be generated by a stochastic process. The main result gives the prior distribution of several AFTs expressed in terms of the true Fourier transform and variants of the characteristic function of the sampling time distribution. The result extends leakage and alias suppression with bias and variance terms due to NUS. Specific sampling processes as described in literature are analyzed in detail. The results are illustrated on simulated signals, with particular focus to the implications for spectral estimation. In non-uniform sampling (NUS), signal amplitudes and time stamps are delivered in pairs. Several methods to compute an approximate Fourier transform (AFT) have appeared in literature, and their posterior properties in terms of alias suppression and leakage have been addressed. In this paper, the sampling times are assumed to be generated by a stochastic process. The main result gives the prior distribution of several AFTs expressed in terms of the true Fourier transform and variants of the characteristic function of the sampling time distribution. The result extends leakage and alias suppression with bias and variance terms due to NUS. Specific sampling processes as described in literature are analyzed in detail. The results are illustrated on simulated signals, with particular focus to the implications for spectral estimation.

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“…Another twist is to design sampling times to minimize aliasing. For stochastic sampling jitter, the distribution of s(t + τ k ) is derived in [2,3] and [14]. These results will be used and extended in this paper.…”

Section: Introductionmentioning

confidence: 99%

Identification with stochastic sampling time jitter

Eng

Gustafsson

2008

Automatica

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This work investigates how stochastic unmeasurable sampling jitter noise affects the result of system identification, and proposes a modification of known approaches to mitigate the effects of sampling jitter. By just assuming conventional additive measurement noise, the analysis shows that the identified model will get a bias in the transfer function amplitude that increases for higher frequencies. A frequency domain approach with a continuous time system model allows an analysis framework for sampling jitter noise. The bias and covariance in the frequency domain model are derived. This is used in bias compensated (weighted) least squares algorithms, and by asymptotic arguments this leads to a maximum likelihood algorithm. Continuous time output error models are used for numerical illustrations.Keywords: system identification, stochastic systems, least-squares estimation, maximum likelihood, frequency domain Identification with Stochastic Sampling Time Jitter ⋆ Frida Eng1 , Fredrik Gustafsson. Dept of EE, Linköpings universitet, SE-58183 Linköping, Sweden AbstractThis work investigates how stochastic unmeasureable sampling jitter noise affects the result of system identification, and proposes a modification of known approaches to mitigate the effects of sampling jitter. By just assuming conventional additive measurement noise, the analysis shows that the identified model will get a bias in the transfer function amplitude that increases for higher frequencies. A frequency domain approach with a continuous time system model allows an analysis framework for sampling jitter noise. The bias and covariance in the frequency domain model are derived. This is used in bias compensated (weighted) least squares algorithms, and by asymptotic arguments this leads to a maximum likelihood algorithm. Continuous time output error models are used for numerical illustrations.

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System Identification Using Measurements Subject to Stochastic Time Jitter

Cited by 4 publications

References 5 publications

Identification of Pharmacokinetics Models in the Presence of Timing Noise

Identification of Pharmacokinetics Models in the Presence of Timing Noise

Frequency Domain Analysis of Signals With Stochastic Sampling Times

Identification with stochastic sampling time jitter

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