Multiaspect classification of airborne targets via physics-based HMMs and matching pursuits


Journal Article

Wideband electromagnetic fields scattered from N distinct target-sensor orientations are employed for classification of airborne targets. Each of the scattered waveforms is parsed via physics-based matching pursuits, yielding N feature vectors. The feature vectors are submitted to a hidden Markov model (HMM), each state of which is characterized by a set of target-sensor orientations over which the associated feature vectors are relatively stationary. The N feature vectors extracted from the multiaspect scattering data implicitly sample N states of the target (some states may be sampled more than once), with the state sequence modeled statistically as a Markov process, resulting in an HMM due to the "hidden" or unknown target orientation. In the work presented here, the state-dependent probability of observing a given feature vector is modeled via physics-motivated linear distributions, in lieu of the traditional Gaussian mixtures applied in classical HMMs. Further, we develop a scheme that yields autonomous definitions for the aspect-dependent HMM states. The paradigm is applied to synthetic scattering data for two simple targets.

Full Text

Duke Authors

Cited Authors

  • Bharadwaj, P; Runkle, P; Carin, L; Berrie, JA; Hughes, JA

Published Date

  • January 1, 2001

Published In

Volume / Issue

  • 37 / 2

Start / End Page

  • 595 - 606

International Standard Serial Number (ISSN)

  • 0018-9251

Digital Object Identifier (DOI)

  • 10.1109/7.937471

Citation Source

  • Scopus