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

Y. Dong, P. Runkle, L. Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


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.
Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Number of pages4
StatePublished - Sep 26 2001
Externally publishedYes

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Generated from Scopus record by KAUST IRTS on 2021-02-09


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