Experimental Microwave Target Identification Using Machine Learning
| Autor: | Ala-Addin Nabulsi, Somen Baidya, Kyle Hetjmanek, Ahmed M. Hassan, Waleed Al-Shaikhli, Reza Derakhshani, George Scott, Clayton Kettlewell, Willig Blake |
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| Rok vydání: | 2019 |
| Předmět: |
Biometrics
business.industry Computer science 020208 electrical & electronic engineering 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Electromagnetic radiation Circular buffer Identification (information) Statistical classification 0202 electrical engineering electronic engineering information engineering Range (statistics) Artificial intelligence business Penetration depth computer Microwave |
| Zdroj: | 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting. |
| DOI: | 10.1109/apusncursinrsm.2019.8888713 |
| Popis: | Electromagnetic radiation in the microwave range has been recently used in a wide range of biomedical applications due to the reasonable penetration depth, resolution, and relatively low cost of equipment in this frequency range. This paper summarizes our progress in developing an experimental microwave system that can be utilized for the noncontact biometric identification of different individuals. The microwave system consists of 8 antennas, arranged in a circular array around the target to be identified, and interfaced to a Vector Network Analyzer using a switching network. The scattered data from ten different cylindrical targets, with different cross-sections, where measured and machine learning techniques were employed to identify these targets with high accuracy. Future work will include upgrading the system and the classification algorithms for the biometric classification of human subjects. |
| Databáze: | OpenAIRE |
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