A Data Driven Approach for Target Classification Based on Histogram Representation of Radar Cross Section

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Abstract
A new approach for classifying targets based on their radar cross section (RCS) is discussed. The RCS presents unique statistical features depending on the target’s shape, while an incident angle with small random fluctuation is considered. Data sets are generated utilizing Physical Optics simulation of the RCS, and the classification of targets with different shapes is performed by Artificial Neural Network (ANN). The algorithm’s performance is evaluated, especially regarding the robustness against noise on the RCS data. Numerical examples motivated by mm-wave radar applications in driving assistance systems are presented. The results show that the classification algorithm performs promising results and ensures the robustness of the features extracted from histogram definitions of RCS.- Title
- A Data Driven Approach for Target Classification Based on Histogram Representation of Radar Cross Section
- Author
- Coşkun, Aysu
- Bilicz, Sándor
- Date of issue
- 2023
- Access level
- Open access
- Copyright owner
- Szerző
- Conference title
- 1st Workshop on Intelligent Infocommunication Networks, Systems and Services (WI2NS2)
- Conference place
- Budapest
- Conference date
- 2023.02.07
- Language
- en
- Page
- 19 - 24
- Subject
- Radar Cross Section, Physical Optics, Histogram features, Artificial Neural Network
- Version
- Post print
- Identifiers
- DOI: 10.3311/WINS2023-004
- Title of the container document
- 1st Workshop on Intelligent Infocommunication Networks, Systems and Services
- ISBN, e-ISBN
- 978-963-421-902-6
- Document type
- Konferenciaközlemény
- Document genre
- Konferenciacikk
- University
- Budapest University of Technology and Economics
- Faculty
- Faculty of Electrical Engineering and Informatics