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18 April 2022 Source localization based on deep learning of phononic modes
Weidi Wang, Amir Ashkan Mokhtari, Ankit Srivastava, Alireza V. Amirkhizi
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Proceedings Volume PC12048, Health Monitoring of Structural and Biological Systems XVI; PC120480L (2022) https://doi.org/10.1117/12.2612432
Event: SPIE Smart Structures + Nondestructive Evaluation, 2022, Long Beach, California, United States
Abstract
This work introduces the source localization application using a phononic crystal (PC) array. The PC band structure and the eigen-modes are analyzed and utilized for detecting the angle of arrival. The eigen-modes, as the basis functions of the scattering wave, possess strong angle-dependent features, naturally suitable for developing source localization algorithms. An artificial neural network is trained with randomly weighted eigen-modes to achieve deep learning of the modal features and angle dependence. The trained neural network can then accurately identify the incident angle of an unknown scattering signal, with minimal side lobe levels and suppressed main lobe width.
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Weidi Wang, Amir Ashkan Mokhtari, Ankit Srivastava, and Alireza V. Amirkhizi "Source localization based on deep learning of phononic modes", Proc. SPIE PC12048, Health Monitoring of Structural and Biological Systems XVI, PC120480L (18 April 2022); https://doi.org/10.1117/12.2612432
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KEYWORDS
Source localization
Scattering
Artificial neural networks
Calcium
Neural networks
Sensors
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