{"title":"抗DOA失配的MVDR波束形成优化稀疏阵列设计","authors":"Xiangrong Wang, M. Amin","doi":"10.1109/CAMSAP.2017.8313065","DOIUrl":null,"url":null,"abstract":"The adaptive beamforming performance using a given number of antennas can be improved by configuring an optimum sparse array. Existing sparse array designing methods for adaptive beamforming assume prior knowledge of exact source directions-of-arrival (DOAs), which may not be applicable in navigation or other sensing situations. The sensitivity of sparse array configurations against uncertainty of source signal DOAs is examined. We seek the optimum sparse array that is robust to the source angular bias, enabling the output signal-to-noise ratio or signal-to-interference-plus-noise ratio to be minimally degraded. Numerical examples are presented to validate the effectiveness of the configured robust sparse arrays in the presence of arbitrary DOA uncertainties.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"256 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Design of optimum sparse array for robust MVDR beamforming against DOA mismatch\",\"authors\":\"Xiangrong Wang, M. Amin\",\"doi\":\"10.1109/CAMSAP.2017.8313065\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The adaptive beamforming performance using a given number of antennas can be improved by configuring an optimum sparse array. Existing sparse array designing methods for adaptive beamforming assume prior knowledge of exact source directions-of-arrival (DOAs), which may not be applicable in navigation or other sensing situations. The sensitivity of sparse array configurations against uncertainty of source signal DOAs is examined. We seek the optimum sparse array that is robust to the source angular bias, enabling the output signal-to-noise ratio or signal-to-interference-plus-noise ratio to be minimally degraded. Numerical examples are presented to validate the effectiveness of the configured robust sparse arrays in the presence of arbitrary DOA uncertainties.\",\"PeriodicalId\":315977,\"journal\":{\"name\":\"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)\",\"volume\":\"256 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CAMSAP.2017.8313065\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAMSAP.2017.8313065","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design of optimum sparse array for robust MVDR beamforming against DOA mismatch
The adaptive beamforming performance using a given number of antennas can be improved by configuring an optimum sparse array. Existing sparse array designing methods for adaptive beamforming assume prior knowledge of exact source directions-of-arrival (DOAs), which may not be applicable in navigation or other sensing situations. The sensitivity of sparse array configurations against uncertainty of source signal DOAs is examined. We seek the optimum sparse array that is robust to the source angular bias, enabling the output signal-to-noise ratio or signal-to-interference-plus-noise ratio to be minimally degraded. Numerical examples are presented to validate the effectiveness of the configured robust sparse arrays in the presence of arbitrary DOA uncertainties.