Mr. Nicholas A. O'Donoughue Profile

Nicholas A. O'Donoughue

at Carnegie Mellon Univ

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Author

Publications (4)

Proceedings Article | 3 April 2012 Paper

Ultrasonic monitoring of a pipe under operating conditions

Chang Liu, Joel Harley, Nicholas O'Donoughue, Yujie Ying, Martin Altschul, James Garrett, José M. Moura, Irving Oppenheim, Lucio Soibelman

Proceedings Volume 8345, 83450B (2012) https://doi.org/10.1117/12.915040

KEYWORDS: Ultrasonics, Transducers, Signal detection, Environmental sensing, Ferroelectric materials, Received signal strength, Waveguides, Signal processing, Temperature metrology, Receivers

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Proceedings Article | 1 April 2010 Paper

Time reversal for damage detection in pipes

Yujie Ying, Joel Harley, James Garrett, Yuanwei Jin, José M. Moura, Nicholas O'Donoughue, Irving Oppenheim, Lucio Soibelman

Proceedings Volume 7647, 76473S (2010) https://doi.org/10.1117/12.847799

KEYWORDS: Waveguides, Transmitters, Transducers, Ferroelectric materials, Ultrasonics, Semiconducting wafers, Inspection, Damage detection, Receivers, Wave propagation

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Proceedings Article | 1 April 2010 Paper

Cognitive sensor networks for structure defect monitoring and classification using guided wave signals

Yuanwei Jin, Nicholas O'Donoughue, José M. Moura, Joel Harley, James Garrett, Irving Oppenheim, Lucio Soibelman, Yujie Ying, Lin He

Proceedings Volume 7647, 76473T (2010) https://doi.org/10.1117/12.848893

KEYWORDS: Sensors, Sensor networks, Corrosion, Waveguides, Environmental sensing, Transducers, Wave propagation, Ultrasonics, Phased arrays, Classification systems

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Proceedings Article | 14 April 2008 Paper

Target recognition using HRR profile-based incoherent SAR (InSAR) image formation

Nicholas O'Donoughue, Walter Kuklinski, Constantine Arabadjis

Proceedings Volume 6967, 69670F (2008) https://doi.org/10.1117/12.776977

KEYWORDS: Target recognition, Detection and tracking algorithms, Radar, Target detection, Synthetic aperture radar, Interferometric synthetic aperture radar, Image acquisition, Reflectivity, Algorithm development, Kinematics

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Feature-aided target verification is a challenging field of research, with the potential to yield significant increases in the confidence of re-established target tracks after kinematic confusion events. Using appropriate control algorithms airborne multi-mode radars can acquire a library of HRR (High Range Resolution) profiles for targets as they are tracked. When a kinematic confusion event occurs, such as a vehicle dropping below MDV (Minimum Detectable Velocity) for some period of time, or two target tracks crossing, it is necessary to utilize feature-aided tracking methods to correctly associate post-confusion tracks with pre-confusion tracks. Many current HRR profile target recognition methods focus on statistical characteristics of either individual profiles or sets of profiles taken over limited viewing angles. These methods have not proven to be very effective when the pre- and post- confusion libraries do not overlap in azimuth angle. To address this issue we propose a new approach to target recognition from HRR profiles. We present an algorithm that generates 2-D imagery of targets from the pre- and post-confusion libraries. These images are subsequently used as the input to a target recognition/classifier process. Since, center-aligned HRR Profiles, while ideal for processing, are not easily computed in field systems, as they require the airborne platform's center of rotation to line up with the geometric center of the moving target (this is impossible when multiple targets are being tracked), our algorithm is designed to work with HRR profiles that are aligned to the leading edge (the first detection above a threshold, commonly referred to as Edge-Aligned HRR profiles). Our simulated results demonstrate the effectiveness of this method for classifying target vehicles based on simulations using both overlapping and non-overlapping HRR profile sets. The algorithm was tested on several test cases using an input set of .28 m resolution XPATCH generated HRR profiles of 20 test vehicles (civilian and military) at various elevation angles.

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