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首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >Recursive Steering Vector Estimation and Adaptive Beamforming under Uncertainties
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Recursive Steering Vector Estimation and Adaptive Beamforming under Uncertainties

机译：不确定条件下的递归转向矢量估计和自适应波束形成

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摘要

The accurate determination of the steering vector of a sensor array that corresponds to a desired signal is often hindered by uncertainties due to array imperfections, such as the presence of a direction-of-arrival (DOA) estimation error, mutual coupling, array sensor gain/phase uncertainties, and senor position perturbations. Consequently, the performance of conventional beamforming algorithms that use the nominal steering vector may be significantly degraded. A new method for recursively correcting possible deterministic errors in the estimated steering vector is proposed here. It employs the subspace principle and estimates the desired steering vector by using a convex optimization approach. We show that the solution can be obtained in closed form by using the Lagrange multiplier method. As the proposed method is based on an extended version of the conventional orthonormal PAST (OPAST) algorithm, it has low implementation complexity, and moving sources can be handled. In addition, a robust beamformer with a new error bound that uses the proposed steering vector estimate is derived by optimizing the worst case performance of the array after taking the uncertainties of the array covariance matrix into account. This gives a diagonally loaded Capon beamformer, where the loading level is related to the bound of the uncertainty in the array covariance matrix. Numerical results show that the proposed algorithm performs well, especially at high signal-to-noise ratio (SNR) and in the presence of deterministic sensor uncertainties.
机译：由于阵列缺陷（例如到达方向（DOA）估计误差的存在，相互耦合，阵列传感器增益的存在），不确定性通常会妨碍准确确定与所需信号相对应的传感器阵列的操纵向量。 /相位不确定性和传感器位置扰动。因此，使用标称转向矢量的传统波束成形算法的性能可能会大大降低。本文提出了一种新的方法，用于递归校正估计的转向向量中可能的确定性误差。它采用子空间原理，并通过使用凸优化方法来估计所需的转向矢量。我们表明，可以使用拉格朗日乘数法以封闭形式获得解。由于该方法基于常规正交PAST（OPAST）算法的扩展版本，因此实现复杂度较低，并且可以处理移动源。此外，在考虑了阵列协方差矩阵的不确定性之后，通过优化阵列的最坏情况性能，可以得出使用建议的转向矢量估计的具有新误差范围的鲁棒波束形成器。这给出了对角加载的Capon波束形成器，其中加载级别与阵列协方差矩阵中不确定性的范围有关。数值结果表明，该算法性能良好，特别是在信噪比高且传感器不确定的情况下。

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