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Sensor Array Calibration in Presence of Mutual Coupling and Gain/Phase Errors by Combining the Spatial-Domain and Time-Domain Waveform Information of the Calibration Sources

机译:通过组合校准源的空间域和时域波形信息,在存在互耦和增益/相位误差的情况下进行传感器阵列校准

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This paper is concerned with the maximum-likelihood (ML) calibration methods tailored to the antenna arrays whose spatial responses are perturbed by mutual coupling effects and unknown sensor gain/phase responses. Unlike the existing work, the proposed methods are capable of jointly exploiting the spatial-domain information and time-domain waveform information of the calibration sources. Two kinds of numerical optimization algorithm are devised dependent on different array geometries. One is suitable for arbitrary irregular array manifold, while the other applies to some particular uniform arrays. Additionally, based on the maximum a posteriori probability (MAP) criterion, we extend the two algorithms to the scenario where the true values of the calibration source azimuths deviate slightly from the nominal ones with a priori known Gaussian distribution. The Cram6r-Rao bound (CRB) expressions for the unknowns are derived in the absence and presence of the azimuth deviations, respectively. Simulation results support that the performances of the proposed algorithms are preferable to the ones which merely employs the spatial-domain information of the calibration sources, and are able to attain the corresponding CRB.
机译:本文关注针对天线阵列量身定制的最大似然(ML)校准方法,该阵列的空间响应会受到互耦效应和未知的传感器增益/相位响应的干扰。与现有工作不同,所提出的方法能够共同利用校准源的空间域信息和时域波形信息。根据不同的阵列几何形状,设计了两种数值优化算法。一种适用于任意不规则阵列流形,而另一种适用于某些特定的均匀阵列。此外,基于最大后验概率(MAP)标准,我们将这两种算法扩展到以下情况:校准源方位角的真实值与先验已知高斯分布的名义值略有偏离。未知数的Cram6r-Rao结合(CRB)表达式分别在不存在和存在方位角偏差的情况下得出。仿真结果表明,所提出算法的性能优于仅采用标定源空间域信息的算法,并且能够获得相应的CRB。

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