Title :
Applying system identification and neural networks to the efficient discrimination of unexploded ordnance
Author :
Brooks, John W. ; Maier, Mark W. ; Vechinski, Scott R.
Author_Institution :
80 Hartington Drive, Madison, AL, USA
Abstract :
This paper describes system identification and neural network methods which, when applied to simulated noisy radar returns from unexploded ordnance (UXO), result in significantly better performance than more traditional methods. The methods are applicable to wideband ground penetrating radar (GPR) waveforms. The target set consists of a conducting cone and cylinder of similar dimensions. The classification is based on features derived by the singularity expansion method
Keywords :
feature extraction; identification; image classification; military systems; neural nets; conducting cone; cylinder; neural networks; penetration depth; simulated noisy radar returns; singularity expansion; system identification; unexploded ordnance; wideband ground penetrating radar waveforms; Computational modeling; Computer networks; Computer simulation; Drives; Ground penetrating radar; Landmine detection; Neural networks; Resonance; System identification; Transfer functions;
Conference_Titel :
Aerospace Conference, 1997. Proceedings., IEEE
Conference_Location :
Snowmass at Aspen, CO
Print_ISBN :
0-7803-3741-7
DOI :
10.1109/AERO.1997.577993
