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Super-resolution Direction of Arrival Estimation Using a Minimum Mean-Square Error Framework *
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DOI：10.1016/j.sigpro.2023.109164.png)
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Abstract
En 中文
This paper develops a novel sparse direction-of-arrival (DOA) estimation technique that avoids the com-mon requirement of hyperparameters, which are typically difficult to set suitably in practice. Using the presented minimum mean-square error (MMSE) estimation framework, we propose a computationally efficient super-resolution DOA estimator that is implemented using an alternate updating of the spatial power distribution of the signals and of the dictionary matrix using a ridge regression algorithm. The reg-ularization parameter determining the sparsity of the solution is formed from the previous spatial power distribution estimates using a SPICE-based criteria. The method employs an adaptive gridding strategy to avoid the grid mismatch problem. The computational complexity is further reduced by the use of an in-tegrated wideband dictionary, determing the active dictionary in an iterative manner. The method offers high resolution estimates of closely spaced sources even for very low sample support (single snapshot) without assuming prior knowledge of the number of sources. Our evaluations illustrate the preferable performance of the proposed estimator as compared to various state-of-the art estimators, also indicating that the method's performance approaches the Cramer-Rao lower bound (CRB) for the examined problem as the signal-to-noise ratio (SNR) increases. & COPY; 2023 Elsevier B.V. All rights reserved.
Keywords:
Direction-of-arrival (DOA) estimation
Wideband dictionary
Minimum mean-square error (MMSE)
estimation framework
Super-resolution
SPICE
Adaptive gridding strategy
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