Date of Award
3-14-2014
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Operational Sciences
First Advisor
Kenneth W. Bauer, PhD.
Abstract
Accurate combat identification is critical to military interactions. Laser radar for vehicle identification is a rapidly developing field that could possibly assist in combat identification by providing information about operating characteristics of a particular vehicle based on measured vibrations. This research focuses on simulated laser radar data collected from mounted vibrometers on idling vehicles. An approach to identify vehicles using nonlinear autoregressive neural networks for classification is developed and employed. The resulting algorithm combines the trained neural networks across three dimensions of vibration readings. This method offers improved performance over literature in successfully identifying a vehicle through vibration measurements alone.
AFIT Designator
AFIT-ENS-14-M-33
DTIC Accession Number
ADA610293
Recommended Citation
Ward, Marc R., "Automatic Target Recognition Using Nonlinear Autoregressive Neural Networks" (2014). Theses and Dissertations. 694.
https://scholar.afit.edu/etd/694