Landmine detection and classification with complex-valued hybrid neural network using scattering parameters dataset.
Neural networks have been applied to landmine detection from data generated by different kinds of sensors. Real-valued neural networks have been used for detecting landmines from scattering parameters measured by ground penetrating radar (GPR) after disregarding phase information. This paper presents results using complex-valued neural networks, capable of phase-sensitive detection followed by classification. A two-layer hybrid neural network structure incorporating both supervised and unsupervised learning is proposed to detect and then classify the types of landmines. Tests are also reported on a benchmark data.
Yang
Chih-Chung
CC
Department of Electrical Engineering, Pennsylvania State University, University Park, PA 16802, USA.
Bose
N K
NK
D000465:Algorithms; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D011825:Radar; D012637:Security Measures; D012815:Signal Processing, Computer-Assisted; D014857:Warfare