Title :
Vibrometry-based vehicle identification framework using nonlinear autoregressive neural networks and decision fusion
Author :
Ward, Marc R. ; Bihl, Trevor J. ; Bauer, Kenneth W.
Author_Institution :
Dept. of Operational Sci., Air Force Inst. of Technol., Wright-Patterson AFB, OH, USA
Abstract :
This research considers simulated laser radar (LADAR) vibrometry for vehicle identification. Time sampled data is considered for developing multiple nonlinear autoregressive neural network (NARNet) classifier models. Emphasis is placed on robustness to sensor location and using small amounts of data. Decision level fusion is used to combine results from multiple classifiers. Results offer improved classification performance as compared to the literature.
Keywords :
autoregressive processes; military vehicles; neural net architecture; optical radar; sensor fusion; vibration measurement; LADAR vibrometry; NARNet classifier model; decision level fusion; laser radar; nonlinear autoregressive neural networks; sensor location; time sampled data; vibrometry-based vehicle identification framework; Accuracy; Data models; Laser radar; Neural networks; Training; Vehicles; Vibrations; Automatic target recognition; classification algorithms; combat identification; engines; laser radar; neural networks; nonlinear autoregressive neural networks; vehicles; vibrations; vibrometers; vibrometry; vibrometry classification;
Conference_Titel :
Aerospace and Electronics Conference, NAECON 2014 - IEEE National
Print_ISBN :
978-1-4799-4690-7
DOI :
10.1109/NAECON.2014.7045799
