{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/36305"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/36305","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Performance Analysis Of Active and Passive Multi-Array Sonar Networks","abstract":"This work investigates the ideal distribution of sensors in networked arrays. MATLAB models these arrays and simulates the results these networks obtain using active and passive sonar. These results determine the ideal sensor placement for optimal parameter detection and estimation of targets. This work's first part focuses on active sonar networks with a fixed number of sensors in a differing number of arrays. MATLAB simulates the data of these sensors taking into account the geometries and velocities of the arrays and targets, then estimates the parameters of the targets using an elliptical filter, a conventional beamformer, a matched filter and one of three fusion methods. This work compares the performance of each network and fusion method. This work shows that the adding more arrays, regardless of size, enhances the overall performance of the network. It also shows the larger arrays obtain more robust parameter estimation. The second part of this work investigates the effects of uncertainty of the array position and orientation using passive sonar. Two networks, one with 2 32-channel arrays and one with 8 2-channel arrays, estimate a sound source's location using a conventional beamformer. MATLAB simulates the data taking into account the geometries of the arrays and the sound source. The results of these simulations show that when uncertainty of position and orientation increases, the better the smaller arrays estimate the location of the sound source compared to the larger arrays.","abstract_html":"This work investigates the ideal distribution of sensors in networked arrays. MATLAB models these arrays and simulates the results these networks obtain using active and passive sonar. These results determine the ideal sensor placement for optimal parameter detection and estimation of targets. This work's first part focuses on active sonar networks with a fixed number of sensors in a differing number of arrays. MATLAB simulates the data of these sensors taking into account the geometries and velocities of the arrays and targets, then estimates the parameters of the targets using an elliptical filter, a conventional beamformer, a matched filter and one of three fusion methods. This work compares the performance of each network and fusion method. This work shows that the adding more arrays, regardless of size, enhances the overall performance of the network. It also shows the larger arrays obtain more robust parameter estimation. The second part of this work investigates the effects of uncertainty of the array position and orientation using passive sonar. Two networks, one with 2 32-channel arrays and one with 8 2-channel arrays, estimate a sound source's location using a conventional beamformer. MATLAB simulates the data taking into account the geometries of the arrays and the sound source. The results of these simulations show that when uncertainty of position and orientation increases, the better the smaller arrays estimate the location of the sound source compared to the larger arrays.","abstract_has_math":false,"creators":["Gold, Brent Andrew"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Mechanical Engineering","degree_department":"Mechanical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Roan, Michael J."],"committee_members":["Johnson, Martin E.","Toso, Alessandro"],"year":2007,"date_issued":"2007-12-05","date_published":"2007-12-05","updated_at":"2026-07-22T22:20:17Z","subjects":["array fusion","passive sonar","active sonar","Multi-array networks"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-12192007-155951"],"render_values":[{"text":"etd-12192007-155951","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/36305","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Roan, Michael J."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Johnson, Martin E.","Toso, Alessandro"]},{"key":"dc:contributor.department","label":"Department","values":["Mechanical Engineering"]},{"key":"dc:creator","label":"Author","values":["Gold, Brent Andrew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:50:25Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:50:25Z","2008-01-18"]},{"key":"dc:date.issued","label":"Date","values":["2007-12-05"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["array fusion","passive sonar","active sonar","Multi-array networks"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-12192007-155951"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/36305"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This work investigates the ideal distribution of sensors in networked arrays. MATLAB models these arrays and simulates the results these networks obtain using active and passive sonar. These results determine the ideal sensor placement for optimal parameter detection and estimation of targets. This work's first part focuses on active sonar networks with a fixed number of sensors in a differing number of arrays. MATLAB simulates the data of these sensors taking into account the geometries and velocities of the arrays and targets, then estimates the parameters of the targets using an elliptical filter, a conventional beamformer, a matched filter and one of three fusion methods. This work compares the performance of each network and fusion method. This work shows that the adding more arrays, regardless of size, enhances the overall performance of the network. It also shows the larger arrays obtain more robust parameter estimation. The second part of this work investigates the effects of uncertainty of the array position and orientation using passive sonar. Two networks, one with 2 32-channel arrays and one with 8 2-channel arrays, estimate a sound source's location using a conventional beamformer. MATLAB simulates the data taking into account the geometries of the arrays and the sound source. The results of these simulations show that when uncertainty of position and orientation increases, the better the smaller arrays estimate the location of the sound source compared to the larger arrays."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Performance Analysis Of Active and Passive Multi-Array Sonar Networks"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Roan, Michael J."],"dc:contributor.committeemember":["Johnson, Martin E.","Toso, Alessandro"],"dc:contributor.department":["Mechanical Engineering"],"dc:creator":["Gold, Brent Andrew"],"dc:date.accessioned":["2014-03-14T20:50:25Z"],"dc:date.available":["2014-03-14T20:50:25Z","2008-01-18"],"dc:date.issued":["2007-12-05"],"dc:description.abstract":["This work investigates the ideal distribution of sensors in networked arrays. MATLAB models these arrays and simulates the results these networks obtain using active and passive sonar. These results determine the ideal sensor placement for optimal parameter detection and estimation of targets. This work's first part focuses on active sonar networks with a fixed number of sensors in a differing number of arrays. MATLAB simulates the data of these sensors taking into account the geometries and velocities of the arrays and targets, then estimates the parameters of the targets using an elliptical filter, a conventional beamformer, a matched filter and one of three fusion methods. This work compares the performance of each network and fusion method. This work shows that the adding more arrays, regardless of size, enhances the overall performance of the network. It also shows the larger arrays obtain more robust parameter estimation. The second part of this work investigates the effects of uncertainty of the array position and orientation using passive sonar. Two networks, one with 2 32-channel arrays and one with 8 2-channel arrays, estimate a sound source's location using a conventional beamformer. MATLAB simulates the data taking into account the geometries of the arrays and the sound source. The results of these simulations show that when uncertainty of position and orientation increases, the better the smaller arrays estimate the location of the sound source compared to the larger arrays."],"dc:description.degree":["Master of Science"],"dc:identifier.other":["etd-12192007-155951"],"dc:identifier.uri":["http://hdl.handle.net/10919/36305"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["array fusion","passive sonar","active sonar","Multi-array networks"],"dc:title":["Performance Analysis Of Active and Passive Multi-Array Sonar Networks"],"dc:type":["Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:17Z"}