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Accession Number ADA581711
Title Generalizable Hierarchical Bayesian Model for Persistent SAR Change Detection.
Publication Date Apr 2012
Media Count 22p
Personal Author E. G. Zelnio G. E. Newstadt I. A. Hero
Abstract This paper proposes a hierarchical Bayesian model for multiple-pass, multiple antenna synthetic aperture radar (SAR) systems with the goal of adaptive change detection. We model the SAR phenomenology directly, including antenna and spatial dependencies, speckle and specular noise, and stationary clutter. We extend previous work by estimating the antenna covariance matrix directly, leading to improved performance in high clutter regions. The proposed SAR model also is shown to be easily generalizable when additional prior information is available, such as locations of roads/intersections or smoothness priors on the target motion. The performance of our posterior inference algorithm is analyzed over a large set of measured SAR imagery. It is shown that the proposed algorithm provides competitive or better results than common change detection algorithms with additional benefits such as few tuning parameters and a characterization of the posterior distribution.
Keywords Adaptive systems
Antenna arrays
Antenna covariance matrix
Ati(Along-track interferometry)
Bayes theorem
Change detection
Dpca(Displaced phase center array processing)
Gibbs sampling scheme
Hidden markov models
Hierarchical bayesian models
Markov processes
Mathematical models
Monte carlo method
Moving targets
Persistent monitoring
Posterior distribution estimation
Posterior inference
Radar clutter
Radar images
Stationary targets
Synthetic aperture radar
Target detection
Target tracking

Source Agency Non Paid ADAS
NTIS Subject Category 72F - Statistical Analysis
63H - Radiofrequency Detection
63 - Detection & Countermeasures
Corporate Author Michigan Univ., Ann Arbor. Dept. of Electrical Engineering and Computer Science.
Document Type Technical report
Title Note Conference paper.
NTIS Issue Number 1325
Contract Number FA8650-07-D-1220-0006

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