The detection and tracking of small slow moving targets by High Frequency Surface Wave radar are limited by the presence of a dominate sea clutter spectrum. The ocean surface behaves as a distributed source in contrast to targets that are point sources. It is shown that by mapping data to eigenspaces, the sea clutter level decreases due to its nondeterministic behaviour while point targets' levels remain unchanged. The high resolution (subspace-based or eigenspace) methods and frequency tracking method for slowly time varying frequencies are evaluated to suppress this sea clutter to enhance detection of weak signals. Experimental results verify the advantage of subspace-based methods over the traditional processing techniques. Conventional subspace methods can be utilized to enhance the detection, but they deteriorate dramatically in the presence of correlated sea clutter. In our thesis some adaptive sea clutter pre-filtering schemes are introduced which improve the threshold and accuracy of subsequent subspace methods. Both simulated and real ship targets are used to verify the effectiveness of our proposed method. Furthermore, we propose another novel subspace algorithm to estimate the directions of arrival of superimposed cisoidal radar echoes from far-field targets in the radar pulse domain. The improvement provided by this algorithm is based on the use of a state space model that more accurately represents the received Doppler radar array signal prior to spatial processing such as MUSIC. A 2-d (spatial and temporal) pre-filtering matrix is structured and applied to the received array signal, which is finally combined with the high-resolution (MUSIC) method for DOA estimation. Lower resolution threshold and estimation variance are achieved by this algorithm compared to conventional beam-space MUSIC and sensor-space MUSIC. A simplified theoretical resolution threshold is derived, and both the theory and simulations verify the effectiveness of our proposed algorithm. Results from an experiment using a simulated target superimposed on real HF radar sea clutter also confirm the algorithm.

Thesis (M.S. in Applied Physics)--Naval Postgraduate School, December 2009. / Thesis Advisor(s): Borden, Brett. Second Reader: Walters, Donald. "December 2009." Description based on title screen as viewed on January 29, 2010. Author(s) subject terms: Radar imaging, moving targets, point-spread function. Includes bibliographical references (p. 57). Also available in print.

Thesis (M.S. in Combat Systems Technology)--Naval Postgraduate School, December 2004. / Thesis advisor(s): Brett Borden. Includes bibliographical references (p. 71-72). Also available online.

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An important problem in electromagnetics is that of extracting, interpreting, and exploiting scattering mechanisms from the scattered field of a target. Termed “features”, these physics-based descriptions of scattering phenomenology have many and diverse applications such as target identification, classification, validation, and imaging. In this dissertation, the feature extraction, analysis, and exploitation of both synthetic and measured multidimensional radar signatures are investigated. Feature extraction is first performed on simulated data of the highfrequency electromagnetics solver Xpatch. The scattered, far-field of an electrically large target is well-approximated by a discrete set of points known as scattering centers. Xpatch yields three-dimensional (3D) scattering centers of a target one aspect angle at a time by using the shooting and bouncing ray technique and a computer-aided design (CAD) model of the target. The feature extraction technique groups scattering centers across multiple angles that pertain to the same scattering mechanism. Using a nearest neighbor clustering algorithm, this association is carried-out in a multidimensional grid of scattering center angle, bounce, and spatial location, wherein distinct scattering mechanisms are assumed to be non-overlapping. Synthetic monostatic and bistatic feature sets are extracted and analyzed using this algorithm. Additionally, feature sets are exploited to assist humans in electromagnetic CAD model validation. The generation of target CAD models is a challenging, resource-limited, and human-experience-based process. Target features extracted from a CAD model in question are compared individually to measured data from the physical target by projection of their radar signatures. CAD model disagreements such as missing, added, or dimensionally inaccurate components, as well as measurement imperfections are analyzed. Target traceback information of the features identifies flawed areas of the model. The projection value quantifies the degree of disagreement. The feature extraction methodology is next modified for measured radar signatures which lack readily available scattering center and bounce information. First, many ground plane synthetic aperture radar images of overlapping, limited apertures in azimuth are formed from the measurement data. Then, two-dimensional scattering centers of all images are estimated using a modified CLEAN algorithm. Feature extraction is lastly performed as with Xpatch data, though a reduction in grid dimensionality and orthogonality occurs. Finally, measured feature sets are exploited for sparse elevation 3D imaging and improved CAD model validation. The first application estimates the truth 3D scattering center of each feature using linear least squares to then visualize a composite 3D image of the target. The second application projects both synthetic and measured feature radar signatures to mitigate errors from the intersection of features in the combined measurement signature. / text

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Thesis (M.S. in Applied Physics)--Naval Postgraduate School, June 2009. / Thesis Advisor(s): Borden, Brett H. "June 2009." Description based on title screen as viewed on July 14, 2009. Author(s) subject terms: radar imaging, moving targets, point spread function, ambiguity function. Includes bibliographical references (p. 73-75). Also available in print.

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