Markov modeling of transient scattering and its application in multi-aspect target classification

Published

Journal Article

Transient scattered fields from a general target are composed of wavefronts, resonances and time delays, with these constituents linked to the target geometry. A classifier applied transient scattering data requires a statistical model for such fundamental constituents. A Markov model is employed to characterized the transient scattered fields - for a set of target-sensor orientation over which the transient scattering is stationary - utilizing a wavefront, resonance, time-delay "alphabet". The Markov model is utilized in a classifier developed for multi-aspect transient scattering data, with a hidden Markov model (HMM) employed to address the generally non-stationary nature of the multi-aspect waveforms. Each state of the HMM is characteristic of a set of target-sensor orientations for which the scattering statistics are stationary, the statistics of which are characterized via the aforementioned Markov model. The wavefront, resonance and time-delay features are extracted via a modified matching-pursuits algorithm.

Duke Authors

Cited Authors

  • Dong, Y; Runkle, P; Carin, L

Published Date

  • September 26, 2001

Published In

Volume / Issue

  • 5 /

Start / End Page

  • 2841 - 2844

International Standard Serial Number (ISSN)

  • 1520-6149

Citation Source

  • Scopus