In this letter, we propose an enhanced nested array (ENA) configuration consisting of two uniform linear arrays (ULAs) with different separations and an additional sensor, whose resulting difference co-array (DCA) is ...
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In this letter, we propose an enhanced nested array (ENA) configuration consisting of two uniform linear arrays (ULAs) with different separations and an additional sensor, whose resulting difference co-array (DCA) is hole-free. As compared with most of the existing sparse array configurations, the proposed ENA has simple closed-form expressions for array geometry and degrees of freedom (DOF), and also can provide more consecutive DOF and larger array aperture. Based on the above good properties of the ENA, compressive sensing (CS) approach is employed for direction-of-arrival (DOA) estimation by solving an l1-norm minimization problem. The theoretical propositions are proved and numerical simulations are performed to demonstrate the superior performance of the proposed ENA.
Sparse arrays can increase the array aperture and degrees of freedom through the construction of either sum or difference co-arrays or both. In order to exploit the advantages of sparse arrays while estimating directi...
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Sparse arrays can increase the array aperture and degrees of freedom through the construction of either sum or difference co-arrays or both. In order to exploit the advantages of sparse arrays while estimating directions of arrival (DOAs) of a mixture of circular and non-circular signals, in this paper, a gridless DOA estimation method is proposed by employing a recently introduced enhanced nested array, whose virtual arrays have no holes. The virtual signals derived from both sum and difference co-arrays are constructed based on atomic norm minimization. It is shown that the proposed method also works when the circular and non-circular signals come from the same set of directions. Simulation results are provided to demonstrate the performance of the proposed method.
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