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A Generalized Sparse Bayesian Learning Algorithm for 1-bit DOA Estimation
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DOI：10.1109/LCOMM.2018.2834904.png)
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摘要
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In this letter, we consider the direction of arrival (DOA) estimation problem from one-bit quantized measurements in both single and multi snapshot scenarios. First, by formulating the one-bit DOA estimation in the single snapshot as a generalized linear model inference problem, the recently generalized sparse Bayesian learning (Gr-SBL) algorithm is applied to solve it. Then, the Gr-SBL is extended to the multi snapshot scenario by decoupling the multi snapshot one-bit DOA estimation problem into a sequence of the single snapshot sub-problems. Numerical results demonstrate the efficiency of the proposed Gr-SBL algorithms.
Keyword:
DOA
SBL
one-bit quantization
extrinsic information
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