TY - JOUR
T1 - Experimental validation of a data-driven step input estimation method for dynamic measurements
AU - Quintana-Carapia, Gustavo
AU - Markovsky, Ivan
AU - Pintelon, Rik
AU - Csurcsia, Péter Zoltán
AU - Verbeke, Dieter Toon
PY - 2020/7/1
Y1 - 2020/7/1
N2 - 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 Cramér-Rao lower bound for biased estimators.
AB - 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 Cramér-Rao lower bound for biased estimators.
KW - Metrology
KW - Dynamic measurement
KW - Cramér-Rao lower bound,
KW - Data-driven signal processing method
UR - http://www.scopus.com/inward/record.url?scp=85087075053&partnerID=8YFLogxK
U2 - 10.1109/TIM.2019.2951865
DO - 10.1109/TIM.2019.2951865
M3 - Article
VL - 69
SP - 4843
EP - 4851
JO - IEEE Transactions on Instrumentation and measurement
JF - IEEE Transactions on Instrumentation and measurement
SN - 0018-9456
IS - 7
M1 - 8892656
ER -