By Topic

Adaptive multi-aspect target classification and detection with hidden Markov models

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)
Shihao Ji ; Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA ; Xuejun Liao ; Carin, L.

We consider target classification and detection based on backscattered observations measured from a sequence of target-sensor orientations. The multi-aspect scattered waves from a given target are modeled with a hidden Markov model (HMM). The targets are assumed concealed and the absolute target-sensor orientation is assumed unknown; therefore, it is possible to control only the angular displacements (change in orientation) between consecutive measurements. The performance of the HMM classifiers/detectors is influenced by the choice of the angular displacements, the optimization of which motivates the developed adaptive search strategies, based on entropy-driven optimality criteria. The search proceeds in a sequential fashion. Based on the previous observations and their associated angular displacements, one determines the optimal next displacement to perform an associated observation. The search strategies are detailed and example results presented on adaptive classification and detection of underwater targets.

Published in:

Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on  (Volume:2 )

Date of Conference:

17-21 May 2004