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General information |
| Author |
Briones, Janette C.; Flores, Benjamin; Cruz-Cano, Raul |
| Published |
InTech Open Access Publisher, 2012
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| Abstract |
In typical radar systems, the process of
recognizing a target requires human involvement. This
human element makes radar systems not fully reliable due
to unstable performance that varies between operators.
This paper describes an intelligent radar system which
addresses this problem in a border surveillance
environment. The proposed radar system is capable of
automatically detecting and then classifying different
targets using an artificial neural network trained with the
Levenberg‐Marquardt algorithm. The training and test sets
presented to the neural network are composed by
high‐resolution Inverse Synthetic Aperture Radar pictures
obtained by the radar’s detection module. Simulation
results show that the intelligent radar system can reliably
detect and distinguish the different objectives. Moreover,
the radar system can outperform human operators and
another radar system that deals with similar objectives.
These results indicate that future intelligent systems can
potentially replace human radar operators in this critical
security setting. |
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International Journal of Advanced Robotic Systems
Author: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Published: 2004
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