{"title":"基于阵列信号处理嵌套阵列的 DOA 估算技术","authors":"Muye Sun, Tianyu Duanmu","doi":"10.54254/2755-2721/64/20241345","DOIUrl":null,"url":null,"abstract":"Research on non-uniform arrays has always been a focus of attention for scholars both domestically and internationally. Part of the research concentrates on existing non-uniform arrays, while another part focuses on optimizing the position of array elements or expanding the structure. Of course, there are also studies on one-dimensional and two-dimensional DOA estimation algorithms based on array spatial shapes, despite some issues. As long as there is a demand for spatial domain target positioning, the development and refinement of non-uniform arrays will continue to be a hot research direction. Nested arrays represent a unique type of heterogeneous array, whose special geometric shape significantly increases degrees of freedom and enhances estimation performance for directional information of undetermined signal sources. Compared to other algorithms, the one-dimensional DOA estimation algorithm based on spatial smoothing simplifies algorithm complexity, improves estimation accuracy under nested arrays, and can effectively handle the estimation of signal sources under uncertain conditions. The DFT algorithm it employs not only significantly improves angular estimation performance but also reduces operational complexity, utilizing full degrees of freedom to minimize aperture loss. Furthermore, the DFT-MUSIC method greatly reduces algorithmic computational complexity while performing very closely to the spatial smoothing MUSIC algorithm. The sparse arrays it utilizes, including minimum redundancy arrays, coprime arrays, and nested arrays, are a new type of array. Sparse arrays can increase degrees of freedom compared to traditional uniform linear arrays and solve the estimation of signal source angles under uncertain conditions, while also enhancing algorithm angular estimation performance.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"74 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DOA estimation technology based on array signal processing nested array\",\"authors\":\"Muye Sun, Tianyu Duanmu\",\"doi\":\"10.54254/2755-2721/64/20241345\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Research on non-uniform arrays has always been a focus of attention for scholars both domestically and internationally. Part of the research concentrates on existing non-uniform arrays, while another part focuses on optimizing the position of array elements or expanding the structure. Of course, there are also studies on one-dimensional and two-dimensional DOA estimation algorithms based on array spatial shapes, despite some issues. As long as there is a demand for spatial domain target positioning, the development and refinement of non-uniform arrays will continue to be a hot research direction. Nested arrays represent a unique type of heterogeneous array, whose special geometric shape significantly increases degrees of freedom and enhances estimation performance for directional information of undetermined signal sources. Compared to other algorithms, the one-dimensional DOA estimation algorithm based on spatial smoothing simplifies algorithm complexity, improves estimation accuracy under nested arrays, and can effectively handle the estimation of signal sources under uncertain conditions. The DFT algorithm it employs not only significantly improves angular estimation performance but also reduces operational complexity, utilizing full degrees of freedom to minimize aperture loss. Furthermore, the DFT-MUSIC method greatly reduces algorithmic computational complexity while performing very closely to the spatial smoothing MUSIC algorithm. The sparse arrays it utilizes, including minimum redundancy arrays, coprime arrays, and nested arrays, are a new type of array. Sparse arrays can increase degrees of freedom compared to traditional uniform linear arrays and solve the estimation of signal source angles under uncertain conditions, while also enhancing algorithm angular estimation performance.\",\"PeriodicalId\":350976,\"journal\":{\"name\":\"Applied and Computational Engineering\",\"volume\":\"74 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied and Computational Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.54254/2755-2721/64/20241345\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied and Computational Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54254/2755-2721/64/20241345","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
非均匀阵列研究一直是国内外学者关注的焦点。一部分研究集中在现有的非均匀阵列上,另一部分研究则集中在优化阵元位置或扩展阵列结构上。当然,也有基于阵列空间形状的一维和二维 DOA 估计算法的研究,尽管存在一些问题。只要有空间域目标定位的需求,非均匀阵列的发展和完善仍将是一个热门研究方向。嵌套阵列是一种独特的异构阵列,其特殊的几何形状大大增加了自由度,提高了对未确定信号源方向信息的估计性能。与其他算法相比,基于空间平滑的一维 DOA 估计算法简化了算法复杂度,提高了嵌套阵列下的估计精度,能有效处理不确定条件下的信号源估计。它采用的 DFT 算法不仅显著提高了角度估计性能,还降低了操作复杂度,利用全自由度最大限度地减少了孔径损失。此外,DFT-MUSIC 方法大大降低了算法的计算复杂度,同时与空间平滑 MUSIC 算法的性能非常接近。它所利用的稀疏阵列,包括最小冗余阵列、共生阵列和嵌套阵列,是一种新型阵列。与传统的均匀线性阵列相比,稀疏阵列可以增加自由度,解决不确定条件下的信号源角度估计问题,同时还能提高算法的角度估计性能。
DOA estimation technology based on array signal processing nested array
Research on non-uniform arrays has always been a focus of attention for scholars both domestically and internationally. Part of the research concentrates on existing non-uniform arrays, while another part focuses on optimizing the position of array elements or expanding the structure. Of course, there are also studies on one-dimensional and two-dimensional DOA estimation algorithms based on array spatial shapes, despite some issues. As long as there is a demand for spatial domain target positioning, the development and refinement of non-uniform arrays will continue to be a hot research direction. Nested arrays represent a unique type of heterogeneous array, whose special geometric shape significantly increases degrees of freedom and enhances estimation performance for directional information of undetermined signal sources. Compared to other algorithms, the one-dimensional DOA estimation algorithm based on spatial smoothing simplifies algorithm complexity, improves estimation accuracy under nested arrays, and can effectively handle the estimation of signal sources under uncertain conditions. The DFT algorithm it employs not only significantly improves angular estimation performance but also reduces operational complexity, utilizing full degrees of freedom to minimize aperture loss. Furthermore, the DFT-MUSIC method greatly reduces algorithmic computational complexity while performing very closely to the spatial smoothing MUSIC algorithm. The sparse arrays it utilizes, including minimum redundancy arrays, coprime arrays, and nested arrays, are a new type of array. Sparse arrays can increase degrees of freedom compared to traditional uniform linear arrays and solve the estimation of signal source angles under uncertain conditions, while also enhancing algorithm angular estimation performance.