{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/122078"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/122078","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Reduced complexity adaptive beamformers","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","abstract_has_math":false,"creators":["Mittal, Manan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Singer, Andrew C"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12","date_published":"2023-12","updated_at":"2026-07-22T22:25:00Z","subjects":["Compressed Beamformer","Sensor Arrays","Random Projections","Beamforming","Source Separation","Distributed Arrays"],"languages":["en","eng"],"rights":["Copyright 2023 Manan Sanjeev Mittal"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/122078","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Singer, Andrew C"]},{"key":"dc:creator","label":"Author","values":["Mittal, Manan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-12","2023-12-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Compressed Beamformer","Sensor Arrays","Random Projections","Beamforming","Source Separation","Distributed Arrays"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Manan Sanjeev Mittal"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/122078"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","The student, Manan Mittal, accepted the attached license on 2023-12-08 at 11:29.","The student, Manan Mittal, submitted this Thesis for approval on 2023-12-08 at 11:30.","This Thesis was approved for publication on 2023-12-08 at 14:36.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20180 on 2024-03-01 at 13:15:32","In many applications of interest, one may benefit from the use of large sensor arrays to capture the desired signals at multiple spatial locations. However, in complex acoustic environments, signal-dependent approaches like matched filtering have high computational requirements, which can be impractical for real-time applications. In many commercially available systems, the real-time processing requirement restricts the system designers to use signal-independent beamformers, such as a delay and sum beamformer. This work explores the design of hybrid beamforming systems that first use signal-independent beamformers to project the sensor outputs into a space of lower dimension and in turn uses those preprocessed signals to perform signal-dependent beamforming. These systems easily generalize to distributed arrays and can be constrained to meet bandwidth and latency requirements over a network, as well. This work also investigates the use of universal algorithms in order to find a mixture of projections to maximise the signal-to-noise-ratio of the desired signal. The performance of hybrid systems is evaluated in simulation and with real world data. This thesis proposes a system for source separation in reverberant acoustic environments and hypothesizes configurations for beamspace preprocessors. We then extend the system to environments involving a moving listener by designing a hybrid generalized sidelobe canceler and proposing an energy-based beam mixing strategy to combine the outputs of multiple beamformers, which can be identified as an instantaneous Wiener combination. Further, we propose the use of a universal algorithm, in combining the outputs of several hybrid beamformers that can perform nearly as well as the full-complexity beamformer. We then propose using a performance-weighted blend of random preprocessors that is shown to approach the performance of the sensorspace variant over Monte-Carlo trials in a simulated environment. Next, main lobe protection is incorporated into the random projections to derive a universal compressed beamformer that is able to consistently achieve a lower output power than the full-complexity variant in line with Capon's criterion."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Reduced complexity adaptive beamformers"]}]}],"canonical_facts":{"dc:contributor":["Singer, Andrew C"],"dc:creator":["Mittal, Manan"],"dc:date":["2023-12","2023-12-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","The student, Manan Mittal, accepted the attached license on 2023-12-08 at 11:29.","The student, Manan Mittal, submitted this Thesis for approval on 2023-12-08 at 11:30.","This Thesis was approved for publication on 2023-12-08 at 14:36.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20180 on 2024-03-01 at 13:15:32","In many applications of interest, one may benefit from the use of large sensor arrays to capture the desired signals at multiple spatial locations. However, in complex acoustic environments, signal-dependent approaches like matched filtering have high computational requirements, which can be impractical for real-time applications. In many commercially available systems, the real-time processing requirement restricts the system designers to use signal-independent beamformers, such as a delay and sum beamformer. This work explores the design of hybrid beamforming systems that first use signal-independent beamformers to project the sensor outputs into a space of lower dimension and in turn uses those preprocessed signals to perform signal-dependent beamforming. These systems easily generalize to distributed arrays and can be constrained to meet bandwidth and latency requirements over a network, as well. This work also investigates the use of universal algorithms in order to find a mixture of projections to maximise the signal-to-noise-ratio of the desired signal. The performance of hybrid systems is evaluated in simulation and with real world data. This thesis proposes a system for source separation in reverberant acoustic environments and hypothesizes configurations for beamspace preprocessors. We then extend the system to environments involving a moving listener by designing a hybrid generalized sidelobe canceler and proposing an energy-based beam mixing strategy to combine the outputs of multiple beamformers, which can be identified as an instantaneous Wiener combination. Further, we propose the use of a universal algorithm, in combining the outputs of several hybrid beamformers that can perform nearly as well as the full-complexity beamformer. We then propose using a performance-weighted blend of random preprocessors that is shown to approach the performance of the sensorspace variant over Monte-Carlo trials in a simulated environment. Next, main lobe protection is incorporated into the random projections to derive a universal compressed beamformer that is able to consistently achieve a lower output power than the full-complexity variant in line with Capon's criterion."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/122078"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Manan Sanjeev Mittal"],"dc:subject":["Compressed Beamformer","Sensor Arrays","Random Projections","Beamforming","Source Separation","Distributed Arrays"],"dc:title":["Reduced complexity adaptive beamformers"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}