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Sensor array calibration method in presence of gain/phase uncertainties and position perturbations using the spatial- and time-domain information of the auxiliary sources

机译:存在增益/相位不确定性和位置扰动的传感器阵列校准方法,使用辅助源的时域和时域信息

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This paper deals with the problem of active calibration under the existence of sensor gain/phase uncertainties and position perturbations. Unlike many existing eigenstructure-based (also called subspace-based) calibration methods which using the spatial-domain (i.e., angle) information of the auxiliary sources only, our proposed approach enables exploitation of both the spatial- and time-domain knowledge of the sources, and therefore yields better performance than the eigenstructure-based calibration technology. For the purpose of incorporating the time-domain knowledge of the sources into the error calibration, the maximum likelihood criterion is selected as the optimization principle, and a concentrated alternating iteration procedure (called algorithm II) is developed, which has rapid convergence rate and robustness. As a byproduct of this paper, we also provide an eigenstructure-based calibration approach (termed algorithm I), which alternatively minimizes the weighted signal subspace fitting cost function and weighted noise subspace fitting criterion to update the estimates for sensor position perturbations and gain/phase errors in each iteration, respectively. Similar to some previous subspace-based calibration algorithms in the literature, algorithm I is also asymptotically efficient but is more computationally convenient, and can be introduced as benchmark to be compared to algorithm II. Additionally, the Cram,r-Rao bound (CRB) expressions for the sensor gain/phase errors and position perturbations estimates are presented for two situations: (a) the time-domain waveform information of the sources is unavailable, and (b) the time-domain waveform information of the sources is taken as prior knowledge into account. The CRBs for the two cases are also quantitatively compared, and the resulting conclusion demonstrates that by combining the time-domain waveform information of the sources into the calibration algorithm, a significant performance improvement can be achieved. The simulation experiments are conducted to corroborate the advantages of the proposed algorithms as well as the theoretical analysis in this paper.
机译:本文针对传感器增益/相位不确定性和位置扰动存在下的主动校准问题。与许多现有的仅基于本征结构的(也称为基于子空间的)标定方法仅使用辅助源的空间域(即角度)信息不同,我们提出的方法可以利用空间信息的时域和时域知识源,因此比基于本征结构的校准技术具有更好的性能。为了将源的时域知识纳入误差标定,选择最大似然准则作为优化原则,并开发了一种具有快速收敛速度和鲁棒性的集中交替迭代程序(称为算法II)。 。作为本文的副产品,我们还提供了一种基于特征结构的校准方法(称为算法I),该方法可将加权信号子空间拟合成本函数和加权噪声子空间拟合准则最小化,以更新传感器位置扰动和增益/相位的估计值每次迭代中的错误。与文献中一些以前的基于子空间的校准算法相似,算法I也是渐近有效的,但在计算上更方便,并且可以作为基准与算法II进行比较。此外,针对两种情况,给出了传感器增益/相位误差和位置扰动估计值的Cram,r-Rao界(CRB)表达式:(a)源的时域波形信息不可用,以及(b)源的时域波形信息被视为先验知识。还对两种情况下的CRB进行了定量比较,得出的结论表明,通过将源的时域波形信息组合到校准算法中,可以显着提高性能。进行了仿真实验,以验证所提出算法的优势以及本文的理论分析。

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