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Wideband near-field array signal processing using the sparse representation framework




Dinstel, Amanda, author
Azimi-Sadjadi, Mahmood R., advisor
Chong, Edwin, committee member
Pezeshki, Ali, committee member
Breidt, Jay, committee member

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Recently, the field of sparse representation has attracted a great deal of attention from the perspective of target bearing (angle of arrival) estimation. This strategy takes the approach that a target present in a sensor array's field of view may be treated as a sparse signal, e.g. if a discrete grid is defined over the search area, very few of the points in the grid will contain sources. Source localization then reduces to identifying the sparse grid point(s) which correspond to the highest concentration of energy. Tools from the sparse representation framework may be used to provide exceptionally high resolution solutions to this localization problem. In this work, existing sparse representation-based localization concepts are evaluated and extended for use in the specific application of detection and localization of wideband near-field targets in sonar data. While sparse representation offers a high-resolution detection and localization solution, the application of sparse representation-based techniques to the specific problem of sonar signal processing is challenging for several reasons. First, the general sparse representation-based angle of arrival problem formulation arises from a far-field array signal model, which allows source localization to be framed as a problem of identifying the unknown angle of arrival of sources in the search region. In contrast, the underwater targets under consideration in this work lie in the near-field which necessitates consideration of the unknown target range in addition to the unknown bearing angle. Second, a majority of current studies in the field of sparse representation-based source localization focus on narrowband signal processing. A handful of researchers have explored the extension of sparse recovery to the wideband problem, but most of these approaches require assumptions about the structure (i.e. sparsity profile) of the data, and these assumptions are not applicable to the sonar returns studied in this work. Further, sparse representation-based source localization methods suffer from many of the same limitations as traditional sonar processing techniques, such as sensitivity to the effects of sonar platform motion and other sources of measurement error. Such uncertainties may present themselves as perturbations in the observed data, mismatch of the defined search grid, or both, and ultimately serve to degrade the performance of sparse representation-based source localization algorithms. In this work, a near-field, wideband array signal processing method is developed which seeks to overcome these challenges inherent to sonar signal processing by expanding on existing concepts from the sparse representation framework. A comprehensive study was performed to evaluate the capabilities of the proposed sonar processing method for detection and localization of targets present in two sonar data sets, namely the Pond Experiment 2012 (PondEx10) data set, which was collected in a man-made pond facility using a rail-mounted sonar system, and the Davis Point data set, which was collected using the current generation buried object scanning sonar (BOSS) system. Sparse representation-based images were generated using two approaches. In the first, the effects of platform motion and other uncertainties were neglected, while in the second a mismatch compensation algorithm was incorporated to attempt to compensate for basis mismatch introduced by sonar platform motion and other non-ideal effects.


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