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

Priya Bharadwaj, Paul Runkle, Lawrence Carin, Jeffrey A. Berrie, Jeff A. Hughes

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

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.
Original languageEnglish (US)
Pages (from-to)595-606
Number of pages12
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume37
Issue number2
DOIs
StatePublished - Jan 1 2001
Externally publishedYes

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