{"title":"基于网格分裂的加权稀疏贝叶斯波达方向估计方法","authors":"Shuang Wei, Jiyu Lu","doi":"10.1049/sil2.12187","DOIUrl":null,"url":null,"abstract":"<p>An off-grid weighted sparse Bayesian learning algorithm based on grid fission for direction of arrival estimation is proposed. The existing grid fission algorithms can use fewer grid points with variant intervals to estimate the true DOAs. However, their learning processes are based on the traditional sparse Bayesian algorithm, which only assigns the same prior distribution assumption to the signals on all grids, but ignores the difference of signal distribution of different grid points. It will result in inaccurate fission location and fission direction because of the insufficient resolution of the spatial spectrum, reducing the estimation accuracy. Moreover, the fission strategy will cost much computation time due to the increase of grid points. To solve these problems, the proposed algorithm utilises the orthogonality of signal subspace and noise subspace to design the weights for prior signal distribution assumption, making the peaks of spatial spectrum more pronounced and easy to distinguish, using more accurate estimated DOAs and off-grid parameter to determine the fission location and direction. In addition, the fission process deletes redundant grid points to simplify calculations. Compared with the existing grid fission algorithms, the proposed method has superior performance in estimation accuracy and computational time.</p>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2.12187","citationCount":"0","resultStr":"{\"title\":\"Weighted sparse Bayesian method for direction of arrival estimation based on grid fission\",\"authors\":\"Shuang Wei, Jiyu Lu\",\"doi\":\"10.1049/sil2.12187\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>An off-grid weighted sparse Bayesian learning algorithm based on grid fission for direction of arrival estimation is proposed. The existing grid fission algorithms can use fewer grid points with variant intervals to estimate the true DOAs. However, their learning processes are based on the traditional sparse Bayesian algorithm, which only assigns the same prior distribution assumption to the signals on all grids, but ignores the difference of signal distribution of different grid points. It will result in inaccurate fission location and fission direction because of the insufficient resolution of the spatial spectrum, reducing the estimation accuracy. Moreover, the fission strategy will cost much computation time due to the increase of grid points. To solve these problems, the proposed algorithm utilises the orthogonality of signal subspace and noise subspace to design the weights for prior signal distribution assumption, making the peaks of spatial spectrum more pronounced and easy to distinguish, using more accurate estimated DOAs and off-grid parameter to determine the fission location and direction. In addition, the fission process deletes redundant grid points to simplify calculations. Compared with the existing grid fission algorithms, the proposed method has superior performance in estimation accuracy and computational time.</p>\",\"PeriodicalId\":56301,\"journal\":{\"name\":\"IET Signal Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2.12187\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Signal Processing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/sil2.12187\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/sil2.12187","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Weighted sparse Bayesian method for direction of arrival estimation based on grid fission
An off-grid weighted sparse Bayesian learning algorithm based on grid fission for direction of arrival estimation is proposed. The existing grid fission algorithms can use fewer grid points with variant intervals to estimate the true DOAs. However, their learning processes are based on the traditional sparse Bayesian algorithm, which only assigns the same prior distribution assumption to the signals on all grids, but ignores the difference of signal distribution of different grid points. It will result in inaccurate fission location and fission direction because of the insufficient resolution of the spatial spectrum, reducing the estimation accuracy. Moreover, the fission strategy will cost much computation time due to the increase of grid points. To solve these problems, the proposed algorithm utilises the orthogonality of signal subspace and noise subspace to design the weights for prior signal distribution assumption, making the peaks of spatial spectrum more pronounced and easy to distinguish, using more accurate estimated DOAs and off-grid parameter to determine the fission location and direction. In addition, the fission process deletes redundant grid points to simplify calculations. Compared with the existing grid fission algorithms, the proposed method has superior performance in estimation accuracy and computational time.
期刊介绍:
IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more.
Topics covered by scope include, but are not limited to:
advances in single and multi-dimensional filter design and implementation
linear and nonlinear, fixed and adaptive digital filters and multirate filter banks
statistical signal processing techniques and analysis
classical, parametric and higher order spectral analysis
signal transformation and compression techniques, including time-frequency analysis
system modelling and adaptive identification techniques
machine learning based approaches to signal processing
Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques
theory and application of blind and semi-blind signal separation techniques
signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals
direction-finding and beamforming techniques for audio and electromagnetic signals
analysis techniques for biomedical signals
baseband signal processing techniques for transmission and reception of communication signals
signal processing techniques for data hiding and audio watermarking
sparse signal processing and compressive sensing
Special Issue Call for Papers:
Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf