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Uncertainty arises naturally in the dynamic testing of structures due to measurement noise, sensor alignment error, modeling error, etc.
The high statistics data were used to study the response to different particles, perform sensor alignment and measure the longitudinal shower development in the sandwich.
In the work presented here we add a new autonomous agent, called the local fusion agent, to the CS-MAS architecture, addressing specific problems of on-line sensor alignment, registration, bias removal and data fusion.
This Bayesian method not only provides the most probable values of the modal parameters but also assesses the associated posterior uncertainty analytically, which is especially relevant in field vibration tests arising due to measurement noise, sensor alignment error, modelling error, etc.
The artificial measurement offsets are determined by separating these two, considering the sensor alignment.
We have used data acquired in-orbit for precise sensor alignment and offset calibration.
Similar(50)
Relative sensors alignment, performance of the navigation unit, and cross-sensor cueing are found to be key factors to fully exploit the potential of multi-sensor architectures.
System architecture is presented and special emphasis is given to adopted solutions regarding real time integration of sensors and navigation measurements and high accuracy estimation of sensors alignment.
c d, Voltage sensor structure alignment between NavAb WT) (grey) and NavAb(R2G) (cyan) in side view and top view, respectively.
First, we provide a brief review on sensor calibration, alignment and sensor fusion as background material of this research.
a b, Voltage sensor structure alignment between NavAb WT) (grey) and NavAb(R3G) (green) in side view and top view, respectively.
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