Optimized Time-frequency Classification Methods for Intelligent Automatic Jettisoning of Helmet-mounted Display Systems
Year: 2007
Type: article
Abstract: Helmet-Mounted Display Systems (HMDS) improve the effectiveness of air force pilots in combat.The embedded display systems provide the pilot with vital aircraft information, "look and shoot" weapon cueing systems, as well as night vision capability.However the increased weight and shift in center of gravity due to these systems significantly contribute to the probability of a neck injury to the pilot during the event of an ejection or crash.The Air Force Research Laboratory (AFRL) developed a mechanical system to automatically release the HMDS based on measured acceleration/force to the head.However the acceleration/force measurements during a pilot's normal air combat maneuvering (ACM) can be near peak accelerations seen during a crash or ejection.The acceleration signals measured from the two environments can be modeled as non-stationary random signals with time-varying and unknown statistics.Time-frequency distributions have been shown to be an effective tool in the analysis and classification of non-stationary signals.In this thesis we present two basic approaches to the optimization of time-frequency distributions with the objective of classifying between a set of non-stationary signals.The approaches are independent of any statistical model of the environments.Experimental results are based on real-life data supplied by the AFRL iii I would like to give my deep thanks to my advisor Dr.Howard Fan for his constant support, guidance, and encouragement throughout my studies.He has always managed to find time to provide me with invaluable advice.It was my honor as well as my pleasure to work with him.I would always like to also thank Dr. Carla Purdy and Dr. Raj Bhatnagar for serving on my committee and providing me with insightful comments.I would like to give my gratitude to my family.I am forever thankful to my father
Author: Hatim F. Alqadah
Institution: University of Cincinnati
Language: English
Subfield: Applied Mathematics
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2602651437