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An iterative optimization for mutual coupling correction of UCA with single snapshot

机译:单快照UCA相互耦合校正的迭代优化

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For the case of the single snapshot, the integrated SNR gain could not be obtained without the multiple snapshots, which degrades the mutual coupling correction performance under the lower SNR case. In this paper, an iterative optimization approach is proposed for the mutual coupling correction of the uniform circular array (UCA) with single snapshot. It uses a novel iterative approach to improve the self-calibration algorithm for the mutual coupling correction. Neither the prior knowledge of the correction matrix initialization nor the calibration source with the known position is required for the proposed approach. An optimum solution for the approximation between the no mutual coupling covariance matrix and the covariance matrix with the coupling is derived. Moreover, a global optimization problem is formed for the mutual coupling correction and the spatial spectrum estimation. To avoid solving the nonconvex optimization problem, a chain of the convex optimization is adopted. The simulation results demonstrate the effectiveness of the proposed method, which improve the resolution ability and the estimation accuracy of the multi sources with the single snapshot.
机译:对于单快照的情况,没有多个快照就无法获得集成的SNR增益,这会降低SNR较低情况下的互耦校正性能。本文针对单快照统一圆形阵列(UCA)的相互耦合校正,提出了一种迭代优化方法。它使用一种新颖的迭代方法来改进自校准算法,以进行相互耦合校正。对于所提出的方法,既不需要校正矩阵初始化的先验知识,也不需要具有已知位置的校准源。推导了无互耦协方差矩阵与带耦合的协方差矩阵之间近似的最佳解。而且,形成了用于相互耦合校正和空间谱估计的全局最优化问题。为避免求解非凸优化问题,采用了凸优化链。仿真结果证明了该方法的有效性,提高了单快照多源分解的能力和估计精度。

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