Abstract
Coprime array with sensors can achieve an increased degrees-of-freedom (DOF) of 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.
Highlights
Direction-of-arrival (DOA) estimation is a vital technology in the field of array signal processing that has been widely applied in radar, sonar, acoustic, navigation, and wireless communication [1,2,3].Traditionally, uniform linear array (ULA) has been the most commonly used array configuration because of its simplicity for application and well-developed techniques
Without the requirement for prior information of the miscalibrated sensors, the information received by them is blindly treated as outliers
The maximum correntropy sensors, the information received by them is blindly treated as outliers
Summary
Direction-of-arrival (DOA) estimation is a vital technology in the field of array signal processing that has been widely applied in radar, sonar, acoustic, navigation, and wireless communication [1,2,3]. Under the framework of SBL, Lu et al proposed a method for nested array calibration [21], which employs the EM algorithm to solve a non-convex optimization problem and jointly estimates the DOAs with the error parameters. This method is able to exploit the enhanced DOF offered by the nested array; it is computationally intensive and its convergence is not guaranteed. [22], in this paper, a robust CS-based DOA estimation method is proposed for coprime array in the presence of miscalibrated sensors, which can be used to find more sources than sensors.
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