High-Precision FIR-Model-Based Dynamic Weighing System

Niedźwiecki, Maciej; Pietrzak, Przemysław

doi:10.1109/tim.2016.2575180

Cited by 12 publications

(5 citation statements)

References 20 publications

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“…The sensor dynamics are considered in the design of finite and infinite impulse response compensation digital filters based on deconvolution [8] or synthesized to correct dynamic errors [5]. The model-based deconvolution design of compensators implies that the measurand true value should be known a-priori for certain applications, such as mass determinations [7,9]. In the literature most of the measurements systems are assumed linear time-invariant, but the compensation digital filters can be linear [10], nonlinear [6] or time-varying [11].…”

Section: Introductionmentioning

confidence: 99%

Input parameters estimation from time-varying measurements

Quintana-Carapia

Markovsky

2020

Measurement

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A measurement is a dynamical process that aims to estimate the true value of a measurand. The measurand is the input that excites a sensor, and, as a consequence, the sensor output is a transient response. The main approach to estimate the input is applying the sensor transient response to another dynamical system. This dynamical system is designed by deconvolution to invert the sensor dynamics and compensate the sensor response. Digital signal processors enable an alternative approach to estimate the unknown input. There exists a data-driven subspace-based signal processing method that estimates a measurand, assuming it is constant during the measurement. To estimate the parameters of a measurand that varies at a constant rate, we extended the data-driven input estimation method to make it adaptive to the affine input.In this paper, we describe the proposed subspace signal processing method for the measurement of an affine measurand and compare its performance to a maximum-likelihood input estimation method and to an existing time-varying compensation filter. The subspace method is recursive and allows real-time implementations since it directly estimates the input without identifying a sensor model. The maximum-likelihood method is model-based and requires very high computational effort.In this form, the maximum-likelihood method cannot be implemented in real-time, however, we used it as a reference to evaluate the subspace method and the time-varying compensation filter results. The effectiveness of the subspace method is validated in a simulation study with a time-varying sensor. The results show that the subspace method estimation has relative errors that are one order of magnitude smaller and converges two times faster than the compensation filter.

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Section: Introductionmentioning

confidence: 99%

Input parameters estimation from time-varying measurements

Quintana-Carapia

Markovsky

2020

Measurement

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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 shortening of the transient behavior is directly related to a reduction of rise-time. Through the use of LTV filters, several applications are reported to be used on a variety of fields of circuits, systems and signal processing such as: dynamic weighing systems [3], software defined radio (SDR) [4], sensors for biomedical applications [5], channel equalizers [6], inertial sensors [7] and radar processing [8], for instance.…”

Section: Introductionmentioning

confidence: 99%

A Variational Approach for Designing Infinite Impulse Response Filters With Time-Varying Parameters

Garza

Gómez

Lamare

et al. 2018

IEEE Trans. Circuits Syst. I

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Filter design with short transient state is a problem encountered in many fields of circuits, systems and signal processing. In this paper a novel low-pass filter design technique with time-varying parameters is introduced in order to minimize the rise-time parameter. Through the use of calculus of variations a method is developed to obtain the optimal closed-form expression for adjusting the parameters. In this context, two cases are addressed. The ideal case in which infinite bandwidth is required and a solution of finite bandwidth. The latter is obtained by means of a proper constraint formulation in the frequency domain. The proposed filter achieves the shortest rise-time and allows better preservation of the edge shape in comparison with other existing filtering methods. The analysis, synthesis and performance of the proposed system is discussed and illustrated with the aid of simulations.Index Terms-Filter design, rise-time, time-varying parameters, calculus of variations.

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High-Precision FIR-Model-Based Dynamic Weighing System

Cited by 12 publications

References 20 publications

Input parameters estimation from time-varying measurements

Input parameters estimation from time-varying measurements

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

A Variational Approach for Designing Infinite Impulse Response Filters With Time-Varying Parameters

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