Uncertainty Aware Deep Neural Network for Multistatic Localization with
Application to Ultrasonic Structural Health Monitoring
- URL: http://arxiv.org/abs/2007.06814v1
- Date: Tue, 14 Jul 2020 04:53:06 GMT
- Title: Uncertainty Aware Deep Neural Network for Multistatic Localization with
Application to Ultrasonic Structural Health Monitoring
- Authors: Ishan D. Khurjekar, Joel B. Harley
- Abstract summary: This paper uses an uncertainty-aware deep neural distribution network framework to learn robust localization models.
We show that the predictive uncertainty scales as environmental uncertainty increases to provide a statistically meaningful metric for assessing localization accuracy.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Guided ultrasonic wave localization uses spatially distributed multistatic
sensor arrays and generalized beamforming strategies to detect and locate
damage across a structure. The propagation channel is often very complex.
Methods can compare data with models of wave propagation to locate damage. Yet,
environmental uncertainty (e.g., temperature or stress variations) often
degrade accuracies. This paper uses an uncertainty-aware deep neural network
framework to learn robust localization models and represent uncertainty. We use
mixture density networks to generate damage location distributions based on
training data uncertainty. This is in contrast with most localization methods,
which output point estimates. We compare our approach with matched field
processing (MFP), a generalized beamforming framework. The proposed approach
achieves a localization error of 0.0625 m as compared to 0.1425 m with MFP when
data has environmental uncertainty and noise. We also show that the predictive
uncertainty scales as environmental uncertainty increases to provide a
statistically meaningful metric for assessing localization accuracy.
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