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Continuous Approximation Based Dimension-Reduced Estimation for Arbitrary Sampling
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DOI：10.1109/LSP.2020.3001757.png)
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Abstract
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Frequency/direction-of-arrival (DOA) estimation via grid searching or sparse representation is time-consuming in 2D cases. Few dimension-reduction methods exist for arbitrary temporal/spatial sampling. In this letter, we propose the Continuous Approximation based Dimension-Reduced Estimation (CADRE) framework to address this issue. By the linear approximation of vectors from a continuous space using only a few bases, dimension reduction is achieved. For some complicated manifolds or realistic scenarios with only a discrete set of steering vectors available, a discrete simplification is also effective. For parameter estimation, parameter-space multiple signal classification and a group-sparse based algorithm are proposed. Simulations verify the superiority of the proposed estimators in both speed and accuracy.
Keywords:
Two dimensional displays
Estimation
Direction-of-arrival estimation
Manifolds
Arrays
Dimensionality reduction
Frequency estimation
DOA estimation
frequency estimation
decoupling
dimension reduction
group sparse
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