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arXiv 2103.03026eess.SP
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基于正则化互补天线切换的自适应稀疏阵列波束形成器设计

Adaptive Sparse Array Beamformer Design by Regularized Complementary Antenna Switching

Xiangrong Wang, Maria Greco, Fulvio Gini

摘要:

在这项工作中,我们提出了一种新的策略,自适应稀疏阵列波束形成器的设计,称为正则化互补天线切换(RCAS),迅速适应阵列配置和激励权重,根据动态环境,以提高干扰抑制。为了实现可实施的阵列重构设计,RCAS在正则化天线切换的框架下进行,从而将整个阵列孔径集体划分为单独的组,并且每组中只有一个天线被打开以与处理通道连接。RCAS首先设计了一组具有良好静态波束图的确定性互补稀疏阵列,并通过在它们之间切换来收集全阵列数据,同时保持弹性干扰抑制。随后,根据从全阵列数据中提取的信息,计算并重新配置为特定环境定制的自适应稀疏阵列。RCAS被设计为一个排他性的基数约束优化,这是重新通过引入一个辅助变量结合分段线性函数来近似$l_0$ -范数函数。提出了一种正则化方法来迭代求解该问题,并消除了对可行初始搜索点的要求。通过严格的理论分析,证明了该算法本质上是原基数约束优化的等价变换。仿真结果验证了所提出的RCAS策略的有效性。

英文摘要:

In this work, we propose a novel strategy of adaptive sparse array beamformer design, referred to as regularized complementary antenna switching (RCAS), to swiftly adapt both array configuration and excitation weights in accordance to the dynamic environment for enhancing interference suppression. In order to achieve an implementable design of array reconfiguration, the RCAS is conducted in the framework of regularized antenna switching, whereby the full array aperture is collectively divided into separate groups and only one antenna in each group is switched on to connect with the processing channel. A set of deterministic complementary sparse arrays with good quiescent beampatterns is first designed by RCAS and full array data is collected by switching among them while maintaining resilient interference suppression. Subsequently, adaptive sparse array tailored for the specific environment is calculated and reconfigured based on the information extracted from the full array data. The RCAS is devised as an exclusive cardinality-constrained optimization, which is reformulated by introducing an auxiliary variable combined with a piece-wise linear function to approximate the $l_0$-norm function. A regularization formulation is proposed to solve the problem iteratively and eliminate the requirement of feasible initial search point. A rigorous theoretical analysis is conducted, which proves that the proposed algorithm is essentially an equivalent transformation of the original cardinality-constrained optimization. Simulation results validate the effectiveness of the proposed RCAS strategy.

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