Adaptive generalized sidelobe cancelers (GSCs) have been widely used to enhance the desired signal and instantaneously suppress interference signal and noise. However, the GSC beamforming method must know the direction of arrival (DOA) of the desired signal in advance. In this paper, we consider the case of a sensor array application in free-field air in which the target signal source is moving, leading to a time-varying DOA problem. Through analysis of the GSC output error signal, we propose an effective method for estimating the time-varying DOA for a GSC. The new method avoids the intensive complexity requirements of conventional DOA estimation algorithms such as the multiple signal classification algorithm and estimation of signal parameters via rotational invariant techniques. In addition, the convergence performance of adaptive GSC algorithms suffers from an error signal in the presence of the desired signal. A simple augmented Kalman filter (AKF) is employed to calculate the beamformer’s weighting coefficients, removing the influence of the desired signal from the GSC output to improve the convergence performance. A simulation evaluation of the signal-to-interference-plus-noise ratio (SINR) revealed that the AKF algorithm combined with the new DOA tracking method has a better convergence rate and SINR performance than other adaptive GSC algorithms of similar complexity such as the standard Kalman filter and recursive least squares.

中文翻译：

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具有时变到达方向估计的阵列传感器自适应广义旁瓣消除器波束形成

自适应广义旁瓣消除器 (GSC) 已被广泛用于增强所需信号并瞬时抑制干扰信号和噪声。但是，GSC 波束成形方法必须提前知道所需信号的到达方向 (DOA)。在本文中，我们考虑在自由场空气中的传感器阵列应用的情况，其中目标信号源正在移动，导致时变 DOA 问题。通过对 GSC 输出误差信号的分析，我们提出了一种估计 GSC 时变 DOA 的有效方法。新方法避免了传统 DOA 估计算法的密集复杂性要求，例如多信号分类算法和通过旋转不变技术估计信号参数。此外，自适应 GSC 算法的收敛性能会在存在所需信号的情况下受到误差信号的影响。采用简单的增广卡尔曼滤波器 (AKF) 来计算波束形成器的加权系数，从 GSC 输出中去除所需信号的影响，以提高收敛性能。对信干噪比（SINR）的仿真评估表明，AKF算法结合新的DOA跟踪方法比其他类似复杂度的自适应GSC算法（如标准）具有更好的收敛速度和SINR性能。卡尔曼滤波器和递归最小二乘法。从 GSC 输出中去除所需信号的影响以提高收敛性能。对信干噪比（SINR）的仿真评估表明，AKF算法结合新的DOA跟踪方法比其他类似复杂度的自适应GSC算法（如标准）具有更好的收敛速度和SINR性能。卡尔曼滤波器和递归最小二乘法。从 GSC 输出中去除所需信号的影响以提高收敛性能。对信干噪比（SINR）的仿真评估表明，AKF算法结合新的DOA跟踪方法比其他类似复杂度的自适应GSC算法（如标准）具有更好的收敛速度和SINR性能。卡尔曼滤波器和递归最小二乘法。