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

fYear

2014

Firstpage

180

Lastpage

185

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Aerospace and Electronics Conference, NAECON 2014 - IEEE National

Print_ISBN

978-1-4799-4690-7

Type

conf

DOI

10.1109/NAECON.2014.7045799

Filename

7045799