Publication Date
2011
Document Type
Dissertation
Committee Members
Fred Garber (Committee Member), Lee Potter (Committee Member), Michael Raymer (Committee Member), Ronald Riechers (Committee Member), Brian Rigling (Advisor)
Degree Name
Doctor of Philosophy (PhD)
Abstract
Recently, the use of micro-Doppler (μD) radar signatures for classification has become an area of focus, in particular for the case of dynamic targets where many components are interacting over time. To fully exploit the signature information, individual scattering centers may be extracted and associated over the full target observation. Due to the complexity of the target signature, the automated analysis is very difficult. However, the availability of ultra-fine resolution or micro-range (μR) resolution along with target scattering knowledge, can aid this process immensely. Here, we describe a feature extraction algorithm which utilizes both μD and μR data. We apply this algorithm to measured data to gain knowledge of dismount-radar phenomenology. Specifically, we associate μD/μR features to physical human components resulting in an intuitive and physically-relevant model. Additionally, we statistically characterize the radar cross-section (RCS) behavior of the individual body features.
Page Count
93
Department or Program
Ph.D. in Engineering
Year Degree Awarded
2011
Copyright
Copyright 2011, all rights reserved. This open access ETD is published by Wright State University and OhioLINK.
