Synthetic aperture sonar imaging using compressive sensing and an ultrasound transducer array

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dc.contributor.advisorWilkinson, Andrew Johnen_ZA
dc.contributor.authorJideani, Josiah Chimnanuen_ZA
dc.date.accessioned2015-01-03T18:06:05Z
dc.date.available2015-01-03T18:06:05Z
dc.date.issued2013en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractCompressive sensing (CS) also known as compressive sampling is a technique used to reconstruct or recover the full-length of a signal with only a few non-adaptive measurements. It is a model-based framework for data acquisition and signal recovery that is based on the principles of sparsity and incoherence. Sparsity refers to the fact that a signal of interest is sparse and compressible and can be represented concisely in a given basis. Incoherence refers to the idea that a sparse signal is spread out in the basis in which it is acquired. A prominent area of application of this technique is tomography such as magnetic resonance imaging (MRI), X-ray CT, and in 3D synthetic aperture radar (SAR) imaging for reconstructing the elevation reflectivity profile. This dissertation describes the investigation into three-dimensional (3D) synthetic aperture sonar (SAS) imaging in air using compressive sampling. In the work, a 3D SAS simulator using compressive sampling was implemented in MATLAB. The effect of the number of baselines as well as the super-resolution factor on the final image was also investigated. A real 3D SAS imaging system was designed and the results were compared with the results of the simulated system. In the system, the SAS data was captured in a multiple transducer (baseline), single-pass configuration with 15 ultrasonic receivers and a single ultrasonic transmitter that operate at about 40 kHz. Signal conditioning circuits for the transmit and receive signals were built on pieces of veroboard. A PC which ran a custom designed LabVIEW virtual instrument (VI) was used for the synchronous transmission and reception of ultrasonic signals, and the control of the SAS platform via the NI PCI-6070E data acquisition card. The received 2D SAS signal from each transducer was focused using the accelerated chirp scaling algorithm. Compressive sensing was applied to a stack of focused 2D SAS images to achieve focusing in the elevation direction. 3D scenes containing point targets were successfully reconstructed in 3D SAS images using this technique with 9 baselines and a super-resolution factor of 3. The results confirm that CS is an effective technique in super-resolution tomographic reconstructions provided the baseline span is small compared to the imaging range. Also for reliable reconstructions, the appropriate super-resolution factor and number of acquisitions must be chosen.en_ZA
dc.identifier.apacitationJideani, J. C. (2013). <i>Synthetic aperture sonar imaging using compressive sensing and an ultrasound transducer array</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/11149en_ZA
dc.identifier.chicagocitationJideani, Josiah Chimnanu. <i>"Synthetic aperture sonar imaging using compressive sensing and an ultrasound transducer array."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2013. http://hdl.handle.net/11427/11149en_ZA
dc.identifier.citationJideani, J. 2013. Synthetic aperture sonar imaging using compressive sensing and an ultrasound transducer array. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Jideani, Josiah Chimnanu AB - Compressive sensing (CS) also known as compressive sampling is a technique used to reconstruct or recover the full-length of a signal with only a few non-adaptive measurements. It is a model-based framework for data acquisition and signal recovery that is based on the principles of sparsity and incoherence. Sparsity refers to the fact that a signal of interest is sparse and compressible and can be represented concisely in a given basis. Incoherence refers to the idea that a sparse signal is spread out in the basis in which it is acquired. A prominent area of application of this technique is tomography such as magnetic resonance imaging (MRI), X-ray CT, and in 3D synthetic aperture radar (SAR) imaging for reconstructing the elevation reflectivity profile. This dissertation describes the investigation into three-dimensional (3D) synthetic aperture sonar (SAS) imaging in air using compressive sampling. In the work, a 3D SAS simulator using compressive sampling was implemented in MATLAB. The effect of the number of baselines as well as the super-resolution factor on the final image was also investigated. A real 3D SAS imaging system was designed and the results were compared with the results of the simulated system. In the system, the SAS data was captured in a multiple transducer (baseline), single-pass configuration with 15 ultrasonic receivers and a single ultrasonic transmitter that operate at about 40 kHz. Signal conditioning circuits for the transmit and receive signals were built on pieces of veroboard. A PC which ran a custom designed LabVIEW virtual instrument (VI) was used for the synchronous transmission and reception of ultrasonic signals, and the control of the SAS platform via the NI PCI-6070E data acquisition card. The received 2D SAS signal from each transducer was focused using the accelerated chirp scaling algorithm. Compressive sensing was applied to a stack of focused 2D SAS images to achieve focusing in the elevation direction. 3D scenes containing point targets were successfully reconstructed in 3D SAS images using this technique with 9 baselines and a super-resolution factor of 3. The results confirm that CS is an effective technique in super-resolution tomographic reconstructions provided the baseline span is small compared to the imaging range. Also for reliable reconstructions, the appropriate super-resolution factor and number of acquisitions must be chosen. DA - 2013 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2013 T1 - Synthetic aperture sonar imaging using compressive sensing and an ultrasound transducer array TI - Synthetic aperture sonar imaging using compressive sensing and an ultrasound transducer array UR - http://hdl.handle.net/11427/11149 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/11149
dc.identifier.vancouvercitationJideani JC. Synthetic aperture sonar imaging using compressive sensing and an ultrasound transducer array. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2013 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/11149en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherEngineeringen_ZA
dc.titleSynthetic aperture sonar imaging using compressive sensing and an ultrasound transducer arrayen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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