{"title":"通过联合稀疏传感器位置和法罗结构稀疏设计多项式波束成形器","authors":"Caizhi Wang;Huawei Chen;Yanwen Li","doi":"10.1109/JSEN.2024.3421270","DOIUrl":null,"url":null,"abstract":"Polynomial beamformers for microphone arrays, which employ the well-known Farrow structure in digital filters, have drawn interest in recent years due to their capability of dynamic beam steering via simple online parameter tuning. Nevertheless, the computational complexity of the polynomial beamformers is higher than that of the nonsteerable counterpart. Moreover, the computational burden will become more demanding when used with the conventional uniform-spaced arrays for high-quality sound signal acquisition, because a large number of sensors are required due to the limitation imposed by the spatial Nyquist criterion. To address the problem, in this article, we propose to design sparse polynomial beamformers by jointly sparsifying sensor locations and Farrow structures. However, the joint sparse design problem is rather challenging due to the complex structure of the polynomial beamformers. We propose an efficient algorithm to solve the high-dimensional joint sparse design problem using the alternating direction method of multipliers (ADMM). Under the ADMM framework, we first reduce the original high-dimensional optimization problem into a set of subproblems much easier to solve. Then, we theoretically derive the analytical solutions to the subproblems, which are the key to the proposed ADMM algorithm. It is shown that the proposed design algorithm has a much lower computational complexity than the convex programming-based optimization approach widely employed in sparse array (SA) design. The effectiveness of the proposed joint sparse design is evaluated by the design examples as well as through its application in speech enhancement.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sparse Design of Polynomial Beamformers by Jointly Sparsifying Sensor Locations and Farrow Structures\",\"authors\":\"Caizhi Wang;Huawei Chen;Yanwen Li\",\"doi\":\"10.1109/JSEN.2024.3421270\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Polynomial beamformers for microphone arrays, which employ the well-known Farrow structure in digital filters, have drawn interest in recent years due to their capability of dynamic beam steering via simple online parameter tuning. Nevertheless, the computational complexity of the polynomial beamformers is higher than that of the nonsteerable counterpart. Moreover, the computational burden will become more demanding when used with the conventional uniform-spaced arrays for high-quality sound signal acquisition, because a large number of sensors are required due to the limitation imposed by the spatial Nyquist criterion. To address the problem, in this article, we propose to design sparse polynomial beamformers by jointly sparsifying sensor locations and Farrow structures. However, the joint sparse design problem is rather challenging due to the complex structure of the polynomial beamformers. We propose an efficient algorithm to solve the high-dimensional joint sparse design problem using the alternating direction method of multipliers (ADMM). Under the ADMM framework, we first reduce the original high-dimensional optimization problem into a set of subproblems much easier to solve. Then, we theoretically derive the analytical solutions to the subproblems, which are the key to the proposed ADMM algorithm. It is shown that the proposed design algorithm has a much lower computational complexity than the convex programming-based optimization approach widely employed in sparse array (SA) design. The effectiveness of the proposed joint sparse design is evaluated by the design examples as well as through its application in speech enhancement.\",\"PeriodicalId\":447,\"journal\":{\"name\":\"IEEE Sensors Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Journal\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10591619/\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/10591619/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Sparse Design of Polynomial Beamformers by Jointly Sparsifying Sensor Locations and Farrow Structures
Polynomial beamformers for microphone arrays, which employ the well-known Farrow structure in digital filters, have drawn interest in recent years due to their capability of dynamic beam steering via simple online parameter tuning. Nevertheless, the computational complexity of the polynomial beamformers is higher than that of the nonsteerable counterpart. Moreover, the computational burden will become more demanding when used with the conventional uniform-spaced arrays for high-quality sound signal acquisition, because a large number of sensors are required due to the limitation imposed by the spatial Nyquist criterion. To address the problem, in this article, we propose to design sparse polynomial beamformers by jointly sparsifying sensor locations and Farrow structures. However, the joint sparse design problem is rather challenging due to the complex structure of the polynomial beamformers. We propose an efficient algorithm to solve the high-dimensional joint sparse design problem using the alternating direction method of multipliers (ADMM). Under the ADMM framework, we first reduce the original high-dimensional optimization problem into a set of subproblems much easier to solve. Then, we theoretically derive the analytical solutions to the subproblems, which are the key to the proposed ADMM algorithm. It is shown that the proposed design algorithm has a much lower computational complexity than the convex programming-based optimization approach widely employed in sparse array (SA) design. The effectiveness of the proposed joint sparse design is evaluated by the design examples as well as through its application in speech enhancement.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
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