| Summary: | Coprime array with <inline-formula> <math display="inline"> <semantics> <mrow> <mi>M</mi> <mo>+</mo> <mi>N</mi> </mrow> </semantics> </math> </inline-formula> sensors can achieve an increased degrees-of-freedom (DOF) of <inline-formula> <math display="inline"> <semantics> <mrow> <mi mathvariant="script">O</mi> <mrow> <mo>(</mo> <mrow> <mi>M</mi> <mi>N</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> </inline-formula> for direction-of-arrival (DOA) estimation. Utilizing the compressive sensing (CS)-based DOA estimation methods, the increased DOF offered by the coprime array can be fully exploited. However, when some sensors in the array are miscalibrated, these DOA estimation methods suffer from degraded performance or even failed operation. Besides, the key to the success of CS-based DOA estimation is that every target falls on the predefined grid. Thus, a coarse grid may cause the mismatch problem, whereas a fine grid requires great computational cost. In this paper, a robust CS-based DOA estimation algorithm is proposed for coprime array with miscalibrated sensors. In the proposed algorithm, signals received by the miscalibrated sensors are viewed as outliers, and correntropy is introduced as the similarity measurement to distinguish these outliers. Incorporated with maximum correntropy criterion (MCC), an iterative sparse reconstruction-based algorithm is then developed to give the DOA estimation while mitigating the influence of the outliers. A multiresolution grid refinement strategy is also incorporated to reconcile the contradiction between computational cost and the mismatch problem. The numerical simulation results verify the effectiveness and robustness of the proposed method.
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