Contributions to Robust Adaptive Signal Processing with Application to Space-Time Adaptive Radar
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 pe...
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Virginia Tech
2014
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| Online Access: | http://hdl.handle.net/10919/26972 http://scholar.lib.vt.edu/theses/available/etd-04182007-170510/ |
| Summary: | 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 â 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. === Ph. D. |
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