Intelligent target recognition using micro-Doppler radar signatures

Intelligent target recognition using micro-Doppler radar signatures We present an effective quadratic time-frequency S-method based approach in conjunction with the Viterbi algorithm to extract m-D features. The effectiveness of the S-method in extracting m-D features is demonstrated through the application to indoor and outdoor experimental data sets such as rotating fan and human gait. The Viterbi algorithm for the instantaneous frequency estimation is used to enhance the weak human micro-Doppler features in relatively high noise environments. As such, this paper contributes additional experimental micro-Doppler data and analysis, which should help in developing a better picture of the human gait micro-Doppler research and its applications to indoor and outdoor imaging and automatic gait recognition systems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings of SPIE SPIE

Intelligent target recognition using micro-Doppler radar signatures

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Publisher
SPIE
Copyright
Copyright © 2009 COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
ISSN
0277-786X
eISSN
1996-756X
DOI
10.1117/12.819331
Publisher site
See Article on Publisher Site

Abstract

We present an effective quadratic time-frequency S-method based approach in conjunction with the Viterbi algorithm to extract m-D features. The effectiveness of the S-method in extracting m-D features is demonstrated through the application to indoor and outdoor experimental data sets such as rotating fan and human gait. The Viterbi algorithm for the instantaneous frequency estimation is used to enhance the weak human micro-Doppler features in relatively high noise environments. As such, this paper contributes additional experimental micro-Doppler data and analysis, which should help in developing a better picture of the human gait micro-Doppler research and its applications to indoor and outdoor imaging and automatic gait recognition systems.

Journal

Proceedings of SPIESPIE

Published: May 1, 2009

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