This work combines the physical, kinematic, and statistical properties of targets, clutter, and sensor calibration as manifested in multichannel synthetic aperture radar (SAR) imagery into a unified Bayesian structure that simultaneously estimates 1) clutter distributions and nuisance parameters, and 2) target signatures required for detection/inference. A Monte Carlo estimate of the posterior distribution is provided that infers the model parameters directly from the data with little tuning of algorithm parameters. Performance is demonstrated on both measured/synthetic wide-area datasets.
Moving target inference with bayesian models in SAR imagery
IEEE Transactions on Aerospace and Electronic Systems ; 50 , 3 ; 2004-2018
2014-07-01
1758072 byte
Article (Journal)
Electronic Resource
English
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