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Rank Minimization-Based Toeplitz Reconstruction for DoA Estimation Using Coprime Array
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DOI：10.1109/LCOMM.2021.3075227.png)
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Abstract
En 中文
In this letter, we address the problem of direction finding using coprime array, which is one of the most preferred sparse array configurations. Motivated by the fact that non-uniform element spacing hinders full utilization of the underlying information in the receive signals, we propose a direction-of-arrival (DoA) estimation algorithm based on low-rank reconstruction of the Toeplitz covariance matrix. The atomic-norm representation of the measurements from the interpolated virtual array is considered, and the equivalent dual-variable rank minimization problem is formulated and solved using a cyclic optimization approach. The recovered covariance matrix enables the application of conventional subspace-based spectral estimation algorithms, such as MUSIC, to achieve enhanced DoA estimation performance. The estimation performance of the proposed approach, in terms of the degrees-of-freedom and spatial resolution, is examined. We also show the superiority of the proposed method over the competitive approaches in the root-mean-square error sense.
Keywords:
Estimation
Sensor arrays
Covariance matrices
Direction-of-arrival estimation
Sensors
Minimization
Optimization
Toeplitz matrix
direction of arrival (DoA)
sparse array
parameter estimation
convex optimization
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