Haodong Guo;Hua Chen;Wei Liu;Songjie Yang;Chau Yuen;Hing Cheung So
{"title":"三阶和差分展开:一种基于三阶累积量的阵列扩展策略","authors":"Haodong Guo;Hua Chen;Wei Liu;Songjie Yang;Chau Yuen;Hing Cheung So","doi":"10.1109/TSP.2025.3564930","DOIUrl":null,"url":null,"abstract":"Recently, numerous design schemes for high-order sparse linear arrays (SLAs) have been introduced for underdetermined direction-of-arrival (DOA) estimation based on high-order cumulants, which utilize both difference co-array (DCA) and sum co-array (SCA) of the generator arrays to construct a large consecutive virtual co-array, achieving a significant increase in the number of uniform degrees-of-freedom (uDOFs). However, this processing places high demands on the generator arrays, which require both long consecutive DCA and SCA. In addition, the robustness of the derived array is prone to deterioration, due to reduced redundancy between DCA and SCA. To that end, in this paper, an alternative design scheme for third-order SLAs termed third-order sum-difference expansion (TO-SDE) is proposed, which no longer separates DCA and SCA by a shift factor, but considers them as a unified whole. In so doing, most desirable characteristics of the generator array are preserved, such as the size of consecutive virtual co-array, resistance to mutual coupling, and robustness against sensor failures, while the mapping from the sum-difference co-array based second-order SLAs to the third-order is achieved. By selecting the appropriate generator array, excellent DOA estimation performance can be attained in various scenarios.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2099-2109"},"PeriodicalIF":4.6000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Third-Order Sum-Difference Expansion: An Array Extension Strategy Based on Third-Order Cumulants\",\"authors\":\"Haodong Guo;Hua Chen;Wei Liu;Songjie Yang;Chau Yuen;Hing Cheung So\",\"doi\":\"10.1109/TSP.2025.3564930\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recently, numerous design schemes for high-order sparse linear arrays (SLAs) have been introduced for underdetermined direction-of-arrival (DOA) estimation based on high-order cumulants, which utilize both difference co-array (DCA) and sum co-array (SCA) of the generator arrays to construct a large consecutive virtual co-array, achieving a significant increase in the number of uniform degrees-of-freedom (uDOFs). However, this processing places high demands on the generator arrays, which require both long consecutive DCA and SCA. In addition, the robustness of the derived array is prone to deterioration, due to reduced redundancy between DCA and SCA. To that end, in this paper, an alternative design scheme for third-order SLAs termed third-order sum-difference expansion (TO-SDE) is proposed, which no longer separates DCA and SCA by a shift factor, but considers them as a unified whole. In so doing, most desirable characteristics of the generator array are preserved, such as the size of consecutive virtual co-array, resistance to mutual coupling, and robustness against sensor failures, while the mapping from the sum-difference co-array based second-order SLAs to the third-order is achieved. By selecting the appropriate generator array, excellent DOA estimation performance can be attained in various scenarios.\",\"PeriodicalId\":13330,\"journal\":{\"name\":\"IEEE Transactions on Signal Processing\",\"volume\":\"73 \",\"pages\":\"2099-2109\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2025-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Signal Processing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10981609/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"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 Transactions on Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10981609/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Third-Order Sum-Difference Expansion: An Array Extension Strategy Based on Third-Order Cumulants
Recently, numerous design schemes for high-order sparse linear arrays (SLAs) have been introduced for underdetermined direction-of-arrival (DOA) estimation based on high-order cumulants, which utilize both difference co-array (DCA) and sum co-array (SCA) of the generator arrays to construct a large consecutive virtual co-array, achieving a significant increase in the number of uniform degrees-of-freedom (uDOFs). However, this processing places high demands on the generator arrays, which require both long consecutive DCA and SCA. In addition, the robustness of the derived array is prone to deterioration, due to reduced redundancy between DCA and SCA. To that end, in this paper, an alternative design scheme for third-order SLAs termed third-order sum-difference expansion (TO-SDE) is proposed, which no longer separates DCA and SCA by a shift factor, but considers them as a unified whole. In so doing, most desirable characteristics of the generator array are preserved, such as the size of consecutive virtual co-array, resistance to mutual coupling, and robustness against sensor failures, while the mapping from the sum-difference co-array based second-order SLAs to the third-order is achieved. By selecting the appropriate generator array, excellent DOA estimation performance can be attained in various scenarios.
期刊介绍:
The IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals. The term “signal” includes, among others, audio, video, speech, image, communication, geophysical, sonar, radar, medical and musical signals. Examples of topics of interest include, but are not limited to, information processing and the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.