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Mixture of Factor Analyzers (MoFA) Models for the Design and Analysis of SAR Automatic Target Recognition (ATR) Algorithms

Abdel-Rahman, Tarek
http://rave.ohiolink.edu/etdc/view?acc_num=osu1500625807524146

Abstract Details

2017, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
We study the problem of target classification from Synthetic Aperture Radar (SAR) imagery. Target classification using SAR imagery is a challenging problem due to large variations of target signature as the target aspect angle changes. Previous work on modeling wide angle SAR imagery has shown that point features, extracted from scattering center locations, result in a high dimensional feature vector that lies on a low dimensional manifold. We propose to use rich probabilistic models for these target manifolds to analyze classification performance as a function of Signal-to-noise ratio (SNR) and Bandwidth. We employ Mixture of Factor Analyzers (MoFA) models to approximate the target manifold locally, and use error bounds for the estimation and analysis of classification error performance. We compare our performance predictions with the empirical performance of practical classifiers using simulated wideband SAR signatures of civilian vehicles. We then extend this work to design optimal maximally discriminative projections (MDP) for the manifold structured data. An optimization algorithm is proposed that maximizes the Kullback Leibler (KL)-divergence between two mixture models through optimizing the closed-form "Variational Approximation" of the KL-divergence between the MoFA models. We then propose to generalize our MDP dimensionality reduction technique to multi-class using non-linear constrained optimization through minimax quasi-Newton methods. The proposed MDP algorithm is compared to existing dimensionality reduction techniques using simulated Civilian Vehicles datadome dataset and real-world MSTAR data.
Emre Ertin (Advisor)
Randolph Moses (Committee Member)
Bradley Clymer (Committee Member)
109 p.
Electrical Engineering
Synthetic Aperture Radar, Automatic Target Recognition, Mixture of Factor Analyzers, Dimensionality Reduction

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Citations

  • Abdel-Rahman, T. (2017). Mixture of Factor Analyzers (MoFA) Models for the Design and Analysis of SAR Automatic Target Recognition (ATR) Algorithms [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500625807524146

    APA Style (7th edition)

  • Abdel-Rahman, Tarek. Mixture of Factor Analyzers (MoFA) Models for the Design and Analysis of SAR Automatic Target Recognition (ATR) Algorithms. 2017. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1500625807524146.

    MLA Style (8th edition)

  • Abdel-Rahman, Tarek. "Mixture of Factor Analyzers (MoFA) Models for the Design and Analysis of SAR Automatic Target Recognition (ATR) Algorithms." Doctoral dissertation, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500625807524146

    Chicago Manual of Style (17th edition)

osu1500625807524146
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© 2017, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.