Automatic Target Recognition Using Nonlinear Autoregressive Neural Networks
AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH GRADUATE SCHOOL OF ENGINEERING AND MANAGEMENT
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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.
- Target Direction, Range and Position Finding