Sparse Bayesian Learning for DOA Estimation with Recursive Grid-Refining

التفاصيل البيبلوغرافية
العنوان: Sparse Bayesian Learning for DOA Estimation with Recursive Grid-Refining
المؤلفون: Jinghao Zheng, Fangfang Chen, Lan Wang, Jisheng Dai
المصدر: SAM
بيانات النشر: IEEE, 2018.
سنة النشر: 2018
مصطلحات موضوعية: Estimation, Refining, Computer science, 0202 electrical engineering, electronic engineering, information engineering, 020206 networking & telecommunications, 020201 artificial intelligence & image processing, Cover (algebra), 02 engineering and technology, Linear approximation, Bayesian inference, Grid, Algorithm
الوصف: The modeling error for off-grid direction-of-arrival (DOA) estimation can be alleviated by the technique of linear approximation, but cannot be fully eliminated, especially for coarse grids. In this paper, we not only adopt a linear approximation to cover the off-grid gap, but also try to combine it with the idea that all the sampled locations can be viewed as the adjustable parameters. Then, we utilize an expectation-maximization (EM) to recursively refine the grid points, rather than updating the coefficients in the linear approximation directly. In this way, the refined grid points will tend to the true DOAs after several iterations. Simulation results illustrate that the proposed method can give a fast DOA estimation while remain a reasonable accuracy.
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::cc514279705b83dec12ddf6f4fe523dbTest
https://doi.org/10.1109/sam.2018.8448577Test
رقم الانضمام: edsair.doi...........cc514279705b83dec12ddf6f4fe523db
قاعدة البيانات: OpenAIRE