Title page for ETD etd-04182007-170510

Type of Document Dissertation
Author Schoenig, Gregory Neumann
Author's Email Address gschoenig@ieee.org
URN etd-04182007-170510
Title Contributions to Robust Adaptive Signal Processing with Application to Space-Time Adaptive Radar
Degree PhD
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Mili, Lamine M. Committee Chair
Beex, A. A. Louis Committee Member
Goldstein, J. Scott Committee Member
Picciolo, Michael L. Committee Member
Spitzner, Dan J. Committee Member
Zaghloul, Amir I. Committee Member
  • GM-estimator
  • SINR Convergence
  • Adaptive Signal Processing
  • Robust
Date of Defense 2007-04-12
Availability unrestricted
Classical adaptive signal processors typically utilize assumptions in their derivation. The

presence of adequate Gaussian and independent and identically distributed (i.i.d.) input

data are central among such assumptions. However, classical processors have a tendency

to suffer a degradation in performance when assumptions like these are violated. Worse

yet, such degradation is not guaranteed to be proportional to the level of deviation from

the assumptions. This dissertation proposes new signal processing algorithms based on

aspects of modern robustness theory, including methods to enable adaptivity of presently

non-adaptive robust approaches. The contributions presented are the result of research

performed jointly in two disciplines, namely robustness theory and adaptive signal process-

ing. This joint consideration of robustness and adaptivity enables improved performance in

assumption-violating scenarios – scenarios in which classical adaptive signal processors fail.

Three contributions are central to this dissertation. First, a new adaptive diagnostic tool for

high-dimension data is developed and shown robust in problematic contamination. Second,

a robust data-pre-whitening method is presented based on the new diagnostic tool. Finally,

a new suppression-based robust estimator is developed for use with complex-valued adaptive

signal processing data. To exercise the proposals and compare their performance to state-

of-the art methods, data sets commonly used in statistics as well as Space-Time Adaptive

Processing (STAP) radar data, both real and simulated, are processed, and performance is

subsequently computed and displayed. The new algorithms are shown to outperform their

state-of-the-art counterparts from both a signal-to-interference plus noise ratio (SINR) conver-

gence rate and target detection perspective.

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