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• W3046765144 abstract "Sparse arrays, which can localize multiple sources with less physical sensors, have attracted more attention since they were proposed. However, for optimal performance of sparse arrays, it is usually assumed that the circumstances are ideal. But in practice, the performance of sparse arrays will suffer from the model errors like mutual coupling, gain and phase error, and sensor’s location error, which causes severe performance degradation or even failure of the direction of arrival (DOA) estimation algorithms. In this study, we follow with interest and propose a covariance-based sparse representation method in the presence of gain and phase errors, where a generalized nested array is employed. The proposed strategy not only enhances the degrees of freedom (DOFs) to deal with more sources but also obtains more accurate DOA estimations despite gain and phase errors. The Cramer–Rao bound (CRB) derivation is analyzed to demonstrate the robustness of the method. Finally, numerical examples illustrate the effectiveness of the proposed method from DOA estimation." @default.
• W3046765144 created "2020-08-07" @default.
• W3046765144 creator A5049087396 @default.
• W3046765144 creator A5076447478 @default.
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• W3046765144 date "2020-07-31" @default.
• W3046765144 modified "2023-09-24" @default.
• W3046765144 title "Sparsity-Based DOA Estimation with Gain and Phase Error Calibration of Generalized Nested Array" @default.
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• W3046765144 doi "https://doi.org/10.1155/2020/1720310" @default.
• W3046765144 hasPublicationYear "2020" @default.
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