Towards an ideal data acquisition channel

Pintelon, R.; Rolain, Yves; Bossche, Marc Vanden; Schoukens, Joannes

doi:10.1109/19.50428

Cited by 44 publications

(22 citation statements)

References 3 publications

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“…Signal dispersion [10] is related to the nonlinear variation of the phase of the transfer function with frequency. Although most correction methods aim at taking this effect fully into account [11][12][13][14], to our knowledge there has been no attempt to include it in estimates of the dynamic error. Also, the spectra of pulses are never strictly bandwidth limited and in principle extend to infinity, even if the decay may be rapid.…”

Section: Introductionmentioning

confidence: 99%

A novel method of estimating dynamic measurement errors

Hessling¹

2006

Meas. Sci. Technol.

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A method for estimating an upper bound of the dynamic measurement error in the time domain is derived, starting from the transfer function of the system. Labeled response uncertainty, this dynamic error bound can be included in the conventional measurement uncertainty. A typical system for measuring force, pressure or acceleration is here evaluated using this measure. The linear dynamic error arises for two reasons: Varying amplification and delay with frequency. The latter is analogous to the wellknown bandwidth limiting dispersion of signals in transmission systems. For wide spectrum signals/pulses the asymptotic tails of the spectra may generate the major part of the error. No widespread robust, general and systematic method of quantifying the measurement error caused by these effects exists, despite that they may generate signal distortion far beyond the common prediction.

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Section: Introductionmentioning

confidence: 99%

A novel method of estimating dynamic measurement errors

Hessling¹

2006

Meas. Sci. Technol.

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“…Therefore, the design of a compensator is based on the sensor model and requires a deconvolution [4]. Examples of input estimation using compensation of the sensor transient response include a recursive estimation of the compensator parameters [5], finite impulse response (FIR) [6], [7] filters and infinite impulse response (IIR) filters [8], [9]. The filters in these works estimate in real-time the unknown input value.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of a Data-Driven Step Input Estimation Method for Dynamic Measurements

Quintana-Carapia

Markovsky

Pintelon

et al. 2020

IEEE Trans. Instrum. Meas.

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Simultaneous fast and accurate measurement is still a challenging and active problem in metrology.A sensor is a dynamic system that produces a transient response. For fast measurements, the unknown input needs to be estimated using the sensor transient response. When a model of the sensor exists, standard compensation filter methods can be used to estimate the input. If a model is not available, either an adaptive filter is used or a sensor model is identified before the input estimation. Recently, a signal processing method was proposed to avoid the identification stage and estimate directly the value of a step input from the sensor response. This data-driven step input estimation method requires only the order of the sensor dynamics and the sensor static gain. To validate the data-driven step input estimation method, in this paper, the uncertainty of the input estimate is studied and illustrated on simulation and real-life weighing measurements. The mean squared error of the input estimate was observed near to the Cramér Rao lower bound of the estimation problem.

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“…The inverse of a strictly proper system will be improper, that is, high frequency contents will be amplified. The amplification of high frequency noise should be avoided, as is described in [12]. Here, only the case when the system and its inverse are both stable and causal will be investigated.…”

Section: System Inverse Estimationmentioning

confidence: 99%

Estimating models of inverse systems

Jung

Enqvist

2013

52nd IEEE Conference on Decision and Control

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Abstract-This paper considers the problem of how to estimate a model of the inverse of a system. The use of inverse systems can be found in many applications, such as feedforward control and power amplifier predistortion. The inverse model is here estimated with the purpose of using it in cascade with the system itself, as an inverter. A good inverse model in this setting would be one that, when used in series with the original system, reconstructs the original input. The goal here is to select suitable inputs, experimental conditions and loss functions to obtain a good input estimate. Both linear and nonlinear systems will be discussed.For nonlinear systems, one way to obtain a linearizing prefilter is by Hirschorn's algorithm. It is here shown how to extend this to the postdistortion case, and some formulations of how the pre-or postinverter could be estimated are also presented.

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Towards an ideal data acquisition channel

Cited by 44 publications

References 3 publications

A novel method of estimating dynamic measurement errors

A novel method of estimating dynamic measurement errors

Experimental Validation of a Data-Driven Step Input Estimation Method for Dynamic Measurements

Estimating models of inverse systems

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