By Topic

Radar-based human detection and characterization with non-linear phase modeling

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)

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, in Fourier-based techniques the maximum output signal-to-noise ratio (SNR) is given by targets whose target phase is linear. On the contrary, the phase variation of the human target response is nonlinear. This difference causes a significant loss in SNR, and therefore detection performance. In this paper, two novel, nonlinear phase detector designs based on human modeling are presented. In the first method, only the human torso reflections are modeled and unknown model parameters computed using Maximum Likelihood Estimation. In the second method, the entire human body is modeled as a different parametric model. The expected radar response for each combination of parameter values is stored in a database. An optimal sparse approximation to the data is found using Orthogonal Matching Pursuit. The performance of the proposed techniques and optimal space-time adaptive processing algorithm is compared and target characterization applications examined.

Published in:

Signal Processing and Communications Applications Conference (SIU), 2010 IEEE 18th

Date of Conference:

22-24 April 2010