{"id":{"repo_id":"unsw","oai_identifier":"oai:unsworks.library.unsw.edu.au:1959.4/54512"},"canonical_url":"https://search.dev.ndltd.org/etd/unsw/oai:unsworks.library.unsw.edu.au:1959.4/54512","repository":{"repo_id":"unsw","name":"University of New South Wales","base_url":"https://unsworks.unsw.edu.au/oai/provider"},"display":{"title":"Interference Mitigation by Reconfigurable Adaptive Antenna Array","abstract":"Antenna arrays play an important role in diverse application areas, such as target detection and direction of arrival (DOA) estimation to list a few. Traditional receiver structures with a fixed array configuration are either inefficient due to array redundancies or may incur significant performance loss due to insufficient degrees of freedom (DoFs) under different scenarios. In this dissertation, we propose a novel approach that employs reconfigurable adaptive array strategy by antenna selection in order to reduce the cost while preserving/achieving the best performance. Essentially, our strategy considers the array configuration as another DoF in the receiver design. We use compressive sensing and convex optimization techniques to select an optimum subset of antennas and subsequently the array geometry is reconfigured through a sequence of Radio Frequency switches. We investigate the antenna selection problem for both closed-loop and open-loop adaptive array algorithms, as well as DOA estimation based on different metrics. First, we explore the antenna selection for closed-loop data-dependent adaptive array processing with the metric of maximum output signal to interference plus noise ratio (SINR). We propose a new parameter, called Spatial Correlation Coefficient (SCC) to characterize the spatial separation between the target and interferences in terms of array configuration. We then extend this parameter to a two-dimensional concept, named Spatial Spectral Correlation Coefficient for space-time adaptive processing. We also introduce two selection methods, Correlation Measurement and Difference of Convex Sets, to solve the combinatorial optimization in polynomial time. Second, we consider the large array thinning for open-loop beampattern synthesis with the goal of achieving a desired pattern. To this end, three iterative soft-thresholding based optimization algorithms are proposed and compared. Third, we discuss the problem of optimum array configuration for enhanced DOA estimation. We formulate the Cramer-Rao Bound in terms of selected element positions and present a Dinklebach type algorithm to select an optimum isotropic or directional subarray. Finally, we explore the concept of coarray to arrange the antennas with the metric of high resolution. Different algorithms are adapted and utilized to estimate more sources than the number of physical antennas for both fully and partially augmentable arrays. Extensive simulation and experimental results confirm that: (1) Array configuration significantly affects the adaptive beamforming and DOA estimation performance; (2) There exists considerable redundancy of the uniform receiver array structure, which provides necessity and utility of array thinning with reduced cost and well-preserved performance; (3) Arranging a subset of antennas in quantized, optimum positions can achieve maximum output SINR, least squared error between the synthesized and desired beampatterns, minimum estimation variance or high resolution capability depending on different selection metrics.","abstract_html":"Antenna arrays play an important role in diverse application areas, such as target detection and direction of arrival (DOA) estimation to list a few. Traditional receiver structures with a fixed array configuration are either inefficient due to array redundancies or may incur significant performance loss due to insufficient degrees of freedom (DoFs) under different scenarios. In this dissertation, we propose a novel approach that employs reconfigurable adaptive array strategy by antenna selection in order to reduce the cost while preserving/achieving the best performance. Essentially, our strategy considers the array configuration as another DoF in the receiver design. We use compressive sensing and convex optimization techniques to select an optimum subset of antennas and subsequently the array geometry is reconfigured through a sequence of Radio Frequency switches. We investigate the antenna selection problem for both closed-loop and open-loop adaptive array algorithms, as well as DOA estimation based on different metrics. First, we explore the antenna selection for closed-loop data-dependent adaptive array processing with the metric of maximum output signal to interference plus noise ratio (SINR). We propose a new parameter, called Spatial Correlation Coefficient (SCC) to characterize the spatial separation between the target and interferences in terms of array configuration. We then extend this parameter to a two-dimensional concept, named Spatial Spectral Correlation Coefficient for space-time adaptive processing. We also introduce two selection methods, Correlation Measurement and Difference of Convex Sets, to solve the combinatorial optimization in polynomial time. Second, we consider the large array thinning for open-loop beampattern synthesis with the goal of achieving a desired pattern. To this end, three iterative soft-thresholding based optimization algorithms are proposed and compared. Third, we discuss the problem of optimum array configuration for enhanced DOA estimation. We formulate the Cramer-Rao Bound in terms of selected element positions and present a Dinklebach type algorithm to select an optimum isotropic or directional subarray. Finally, we explore the concept of coarray to arrange the antennas with the metric of high resolution. Different algorithms are adapted and utilized to estimate more sources than the number of physical antennas for both fully and partially augmentable arrays. Extensive simulation and experimental results confirm that: (1) Array configuration significantly affects the adaptive beamforming and DOA estimation performance; (2) There exists considerable redundancy of the uniform receiver array structure, which provides necessity and utility of array thinning with reduced cost and well-preserved performance; (3) Arranging a subset of antennas in quantized, optimum positions can achieve maximum output SINR, least squared error between the synthesized and desired beampatterns, minimum estimation variance or high resolution capability depending on different selection metrics.","abstract_has_math":false,"creators":["Wang, Xiangrong"],"institution":"UNSW, Sydney","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015","date_published":"2015","updated_at":"2026-07-24T05:34:07Z","subjects":["Convex optimization techniques","ReconfigurableAdaptive Antenna Array","Compressive sensing","Spatial Correlation Coefficient"],"languages":["EN"],"rights":["open access","CC BY-NC-ND 3.0","free_to_read"],"rights_urls":["https://purl.org/coar/access_right/c_abf2","https://creativecommons.org/licenses/by-nc-nd/3.0/au/"],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.26190/unsworks/18211"],"render_values":[{"text":"https://doi.org/10.26190/unsworks/18211","href":"https://doi.org/10.26190/unsworks/18211","code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/1959.4/54512","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Wang, Xiangrong"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015"]},{"key":"dc:publisher","label":"Institution","values":["UNSW, Sydney"]},{"key":"dc:type","label":"Dc Type","values":["doctoral thesis","http://purl.org/coar/resource_type/c_db06"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Convex optimization techniques","ReconfigurableAdaptive Antenna Array","Compressive sensing","Spatial Correlation Coefficient"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["EN"]},{"key":"dc:rights","label":"Dc Rights","values":["open access","https://purl.org/coar/access_right/c_abf2","CC BY-NC-ND 3.0","https://creativecommons.org/licenses/by-nc-nd/3.0/au/","free_to_read"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/1959.4/54512","https://unsworks.unsw.edu.au/bitstreams/8dd94821-68ea-41b8-b0ca-d0dce193d93e/download","https://doi.org/10.26190/unsworks/18211"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Antenna arrays play an important role in diverse application areas, such as target detection and direction of arrival (DOA) estimation to list a few. Traditional receiver structures with a fixed array configuration are either inefficient due to array redundancies or may incur significant performance loss due to insufficient degrees of freedom (DoFs) under different scenarios. In this dissertation, we propose a novel approach that employs reconfigurable adaptive array strategy by antenna selection in order to reduce the cost while preserving/achieving the best performance. Essentially, our strategy considers the array configuration as another DoF in the receiver design. We use compressive sensing and convex optimization techniques to select an optimum subset of antennas and subsequently the array geometry is reconfigured through a sequence of Radio Frequency switches. We investigate the antenna selection problem for both closed-loop and open-loop adaptive array algorithms, as well as DOA estimation based on different metrics. First, we explore the antenna selection for closed-loop data-dependent adaptive array processing with the metric of maximum output signal to interference plus noise ratio (SINR). We propose a new parameter, called Spatial Correlation Coefficient (SCC) to characterize the spatial separation between the target and interferences in terms of array configuration. We then extend this parameter to a two-dimensional concept, named Spatial Spectral Correlation Coefficient for space-time adaptive processing. We also introduce two selection methods, Correlation Measurement and Difference of Convex Sets, to solve the combinatorial optimization in polynomial time. Second, we consider the large array thinning for open-loop beampattern synthesis with the goal of achieving a desired pattern. To this end, three iterative soft-thresholding based optimization algorithms are proposed and compared. Third, we discuss the problem of optimum array configuration for enhanced DOA estimation. We formulate the Cramer-Rao Bound in terms of selected element positions and present a Dinklebach type algorithm to select an optimum isotropic or directional subarray. Finally, we explore the concept of coarray to arrange the antennas with the metric of high resolution. Different algorithms are adapted and utilized to estimate more sources than the number of physical antennas for both fully and partially augmentable arrays. Extensive simulation and experimental results confirm that: (1) Array configuration significantly affects the adaptive beamforming and DOA estimation performance; (2) There exists considerable redundancy of the uniform receiver array structure, which provides necessity and utility of array thinning with reduced cost and well-preserved performance; (3) Arranging a subset of antennas in quantized, optimum positions can achieve maximum output SINR, least squared error between the synthesized and desired beampatterns, minimum estimation variance or high resolution capability depending on different selection metrics."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Interference Mitigation by Reconfigurable Adaptive Antenna Array"]}]}],"canonical_facts":{"dc:creator":["Wang, Xiangrong"],"dc:date":["2015"],"dc:description":["Antenna arrays play an important role in diverse application areas, such as target detection and direction of arrival (DOA) estimation to list a few. Traditional receiver structures with a fixed array configuration are either inefficient due to array redundancies or may incur significant performance loss due to insufficient degrees of freedom (DoFs) under different scenarios. In this dissertation, we propose a novel approach that employs reconfigurable adaptive array strategy by antenna selection in order to reduce the cost while preserving/achieving the best performance. Essentially, our strategy considers the array configuration as another DoF in the receiver design. We use compressive sensing and convex optimization techniques to select an optimum subset of antennas and subsequently the array geometry is reconfigured through a sequence of Radio Frequency switches. We investigate the antenna selection problem for both closed-loop and open-loop adaptive array algorithms, as well as DOA estimation based on different metrics. First, we explore the antenna selection for closed-loop data-dependent adaptive array processing with the metric of maximum output signal to interference plus noise ratio (SINR). We propose a new parameter, called Spatial Correlation Coefficient (SCC) to characterize the spatial separation between the target and interferences in terms of array configuration. We then extend this parameter to a two-dimensional concept, named Spatial Spectral Correlation Coefficient for space-time adaptive processing. We also introduce two selection methods, Correlation Measurement and Difference of Convex Sets, to solve the combinatorial optimization in polynomial time. Second, we consider the large array thinning for open-loop beampattern synthesis with the goal of achieving a desired pattern. To this end, three iterative soft-thresholding based optimization algorithms are proposed and compared. Third, we discuss the problem of optimum array configuration for enhanced DOA estimation. We formulate the Cramer-Rao Bound in terms of selected element positions and present a Dinklebach type algorithm to select an optimum isotropic or directional subarray. Finally, we explore the concept of coarray to arrange the antennas with the metric of high resolution. Different algorithms are adapted and utilized to estimate more sources than the number of physical antennas for both fully and partially augmentable arrays. Extensive simulation and experimental results confirm that: (1) Array configuration significantly affects the adaptive beamforming and DOA estimation performance; (2) There exists considerable redundancy of the uniform receiver array structure, which provides necessity and utility of array thinning with reduced cost and well-preserved performance; (3) Arranging a subset of antennas in quantized, optimum positions can achieve maximum output SINR, least squared error between the synthesized and desired beampatterns, minimum estimation variance or high resolution capability depending on different selection metrics."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/1959.4/54512","https://unsworks.unsw.edu.au/bitstreams/8dd94821-68ea-41b8-b0ca-d0dce193d93e/download","https://doi.org/10.26190/unsworks/18211"],"dc:language":["EN"],"dc:publisher":["UNSW, Sydney"],"dc:rights":["open access","https://purl.org/coar/access_right/c_abf2","CC BY-NC-ND 3.0","https://creativecommons.org/licenses/by-nc-nd/3.0/au/","free_to_read"],"dc:subject":["Convex optimization techniques","ReconfigurableAdaptive Antenna Array","Compressive sensing","Spatial Correlation Coefficient"],"dc:title":["Interference Mitigation by Reconfigurable Adaptive Antenna Array"],"dc:type":["doctoral thesis","http://purl.org/coar/resource_type/c_db06"]},"updated_at":"2026-07-24T05:34:07Z"}