{"title":"差频无网格稀疏阵列处理","authors":"Yongsung Park;Peter Gerstoft","doi":"10.1109/OJSP.2024.3425284","DOIUrl":null,"url":null,"abstract":"This paper introduces a DOA estimation method for sources beyond the aliasing frequency. The method utilizes multiple frequencies of sources to exploit the frequency difference between them, enabling processing at a frequency below the aliasing frequency. Gridless sparse processing with atomic norm minimization is derived for DOA using difference frequency (DF). This approach achieves higher DOA resolution than previous DF-DOA estimators by enforcing sparsity in the beamforming spectrum and estimating DOAs in the continuous angular domain. We consider one or more measurements in both time (snapshot) and frequency (DF). We also analyze approaches for considering multiple DFs: multi-DF and multi-DF spectral-averaging. Numerical simulations demonstrate the effective performance of the method compared to existing DF techniques.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"914-925"},"PeriodicalIF":2.9000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10591424","citationCount":"0","resultStr":"{\"title\":\"Difference Frequency Gridless Sparse Array Processing\",\"authors\":\"Yongsung Park;Peter Gerstoft\",\"doi\":\"10.1109/OJSP.2024.3425284\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces a DOA estimation method for sources beyond the aliasing frequency. The method utilizes multiple frequencies of sources to exploit the frequency difference between them, enabling processing at a frequency below the aliasing frequency. Gridless sparse processing with atomic norm minimization is derived for DOA using difference frequency (DF). This approach achieves higher DOA resolution than previous DF-DOA estimators by enforcing sparsity in the beamforming spectrum and estimating DOAs in the continuous angular domain. We consider one or more measurements in both time (snapshot) and frequency (DF). We also analyze approaches for considering multiple DFs: multi-DF and multi-DF spectral-averaging. Numerical simulations demonstrate the effective performance of the method compared to existing DF techniques.\",\"PeriodicalId\":73300,\"journal\":{\"name\":\"IEEE open journal of signal processing\",\"volume\":\"5 \",\"pages\":\"914-925\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10591424\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE open journal of signal processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10591424/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE open journal of signal processing","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10591424/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
摘要
本文介绍了一种针对超出混叠频率的信号源的 DOA 估算方法。该方法利用信号源的多个频率来利用它们之间的频率差,从而实现低于混叠频率的处理。利用差分频率 (DF) 对 DOA 进行无网格稀疏处理,原子规范最小化。这种方法通过强制波束成形频谱的稀疏性和在连续角域中估计 DOA,实现了比以前的 DF-DOA 估计器更高的 DOA 分辨率。我们同时考虑了时间(快照)和频率(DF)方面的一个或多个测量。我们还分析了考虑多个 DF 的方法:多DF 和多DF 频谱平均。数值模拟证明,与现有的 DF 技术相比,该方法的性能非常有效。
Difference Frequency Gridless Sparse Array Processing
This paper introduces a DOA estimation method for sources beyond the aliasing frequency. The method utilizes multiple frequencies of sources to exploit the frequency difference between them, enabling processing at a frequency below the aliasing frequency. Gridless sparse processing with atomic norm minimization is derived for DOA using difference frequency (DF). This approach achieves higher DOA resolution than previous DF-DOA estimators by enforcing sparsity in the beamforming spectrum and estimating DOAs in the continuous angular domain. We consider one or more measurements in both time (snapshot) and frequency (DF). We also analyze approaches for considering multiple DFs: multi-DF and multi-DF spectral-averaging. Numerical simulations demonstrate the effective performance of the method compared to existing DF techniques.