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Improved Active Calibration Algorithms in the Presence of Channel Gain/Phase Uncertainties and Sensor Mutual Coupling Effects

机译:存在通道增益/相位不确定性和传感器互耦效应的改进的主动校准算法

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This paper deals with the problem of active calibration when channel gain/phase uncertainties and sensor mutual coupling effects are simultaneously present. The numerical algorithms used to compensate for array error matrix, which is formed by the product of mutual coupling matrix and channel gain/phase error matrix, are presented especially tailored to uniform linear array (ULA) and uniform circular array (UCA). First, the array spatial responses corresponding to different azimuths are numerically evaluated using a set of time-disjoint auxiliary sources at known locations. Subsequently, a least-squares (LS) minimization model with respect to array error matrix is established. To solve this LS problem, two novel algorithms, namely algorithm I and algorithm II, are developed. In algorithm I, the array error matrix is considered as a whole matrix parameter to be optimized and an explicit closed-form solution to the error matrix is obtained. Compared with some existing algorithms with similar computation framework, algorithm I is able to utilize all potentially linear characteristics of ULA's and UCA's error matrix, and the calibration accuracy can be increased. Unlike algorithm I, algorithm II decomposes the array error matrix into two matrix parameters (i.e., mutual coupling matrix and channel gain/phase error matrix) to be optimized and all (nonlinear) numerical properties of the error matrix can be exploited. Therefore, algorithm II is able to achieve better calibration precision than algorithm I. However, algorithm II is more computationally demanding as a closed-form solution is no longer available and iteration computation is involved. In addition, the compact Cram,r-Rao bound (CRB) expressions for all array error parameters are deduced in the case where auxiliary sources are assumed to be complex circular Gaussian distributed. Finally, the two novel algorithms are appropriately extended to the scenario where non-circular auxiliary sources are used, and the estimation variances of the array error parameters can be further decreased if the non-circularity is properly employed. Simulation experiments show the superiority of the presented algorithms.
机译:当通道增益/相位不确定性和传感器互耦效应同时存在时,本文将解决有源校准问题。提出了用于补偿阵列误差矩阵的数值算法,该算法是由互耦矩阵和通道增益/相位误差矩阵的乘积形成的,特别适合于均匀线性阵列(ULA)和均匀圆形阵列(UCA)。首先,在已知位置使用一组时间不相交的辅助源对对应于不同方位角的阵列空间响应进行数值评估。随后,建立关于阵列误差矩阵的最小二乘(LS)最小化模型。为了解决该LS问题,开发了两种新颖的算法,即算法I和算法II。在算法I中,将阵列误差矩阵视为要优化的整个矩阵参数,并获得了误差矩阵的显式封闭形式解。与某些具有类似计算框架的现有算法相比,算法I能够利用ULA和UCA误差矩阵的所有潜在线性特征,并且可以提高校准精度。与算法I不同,算法II将阵列误差矩阵分解为两个矩阵参数(即互耦合矩阵和通道增益/相位误差矩阵)以进行优化,并且可以利用误差矩阵的所有(非线性)数值特性。因此,算法II能够比算法I获得更好的校准精度。但是,算法II在计算上的要求更高,因为不再有封闭形式的解决方案,并且涉及迭代计算。另外,在假设辅助源是复杂的圆高斯分布的情况下,推导出所有阵列误差参数的紧凑Cram,r-Rao界(CRB)表达式。最后,将两种新颖算法适当地扩展到使用非圆形辅助源的情况,并且如果适当地采用非圆形性,则可以进一步减小阵列误差参数的估计方差。仿真实验表明了所提出算法的优越性。

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