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Experimental Validation of a Data-Driven Step Input Estimation Method for Dynamic Measurements
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DOI：10.1109/TIM.2019.2951865.png)
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Abstract
En 中文
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 article, the uncertainty of the input estimate is studied and illustrated on simulation and real-life weighing measurements. It was found that the predicted mean-squared error of the input estimate is close to an approximate Cramer-Rao lower bound for biased estimators.
Keywords:
Estimation
Uncertainty
Transient response
Statistical analysis
Noise measurement
Signal processing
Velocity measurement
Cramer-Rao lower bound (CRB)
data-driven signal processing method
dynamic measurement
metrology
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