NDLI: Enhanced detection and characterization of human targets via non-linear phase modeling

Content Provider	IEEE Xplore Digital Library
Author	Gurbuz, S.Z. Williams, D.B. Melvin, W.L.
Copyright Year	2010
Description	Author affiliation: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA (Gurbuz, S.Z.; Williams, D.B.) \|\| Georgia Tech Research Institute, Georgia Institute of Technology, Atlanta, GA, USA (Melvin, W.L.)
Abstract	Many current radar-based human detection systems employ some type of Doppler or Fourier-based processing, followed by spectrogram and gait analysis to classify detected targets. However, Fourier-based techniques inherently assume a linear variation in target phase over the aperture, whereas human targets have a highly nonlinear phase history. This mismatch leads to significant loss in SNR and integration gain. In this paper, two novel human-modeling based non-linear phase detectors are presented. The first (ONLP) computes maximum likelihood estimates of unknown parameters of a model of the human torso response, while the second (EnONLP) stores the expected returns of a 12-point model for each combination of model parameter values in a dictionary and uses orthogonal matching pursuit to find the optimal sparse approximation to the data. The performance of ONLP, EnONLP, and conventional STAP is compared and application to target characterization discussed.
Starting Page	183
Ending Page	187
File Size	847496
Page Count	5
File Format	PDF
ISBN	9781424458110
ISSN	10975659
e-ISBN	9781424458127
DOI	10.1109/RADAR.2010.5494630
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Phase detection Humans Radar detection Spectrogram Apertures History Detectors Maximum likelihood detection Maximum likelihood estimation Parameter estimation
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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