Polarimetric classification of scattering centers using M-ary Bayesian decision rules

A Bayes-optimal decision rule is presented for detection and classification of scattering centers in clutter. Scattering centers are modeled as one of M canonical reflectors with unknown amplitude, phase and orientation angle; clutter is modeled as a spherically invariant random vector. A choice of...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2000-07, Vol.36 (3), p.738-749
Main Authors: Ertin, E., Potter, L.C.
Format: Article
Language:English
Subjects:
Angle of reflection
Bayesian analysis
Bayesian methods
Classification
Clutter
Costs
Detectors
Invariants
Polarization
Radar detection
Radar imaging
Radar scattering
Risk
Scattering
Testing
Uncertainty
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Summary:A Bayes-optimal decision rule is presented for detection and classification of scattering centers in clutter. Scattering centers are modeled as one of M canonical reflectors with unknown amplitude, phase and orientation angle; clutter is modeled as a spherically invariant random vector. A choice of costs in the Bayes risk is shown to yield a two-stage classification rule. The first stage is a Neyman-Pearson detector which rejects clutter, whereas the second stage classifies the detection in one of the M target classes. The resulting decision rule yields computationally simple implementation, intuitive geometric interpretation, and posterior estimation of decision uncertainty. Performance of the proposed classifier is illustrated on imagery from an airborne UHF-hand radar.
ISSN:0018-9251
1557-9603
DOI:10.1109/7.869492