摘要翻译：
本文提出了一种新的阵列响应控制方案&复系数权向量正交分解($Textrm{C}^2textrm{-word}$)及其在方向图综合中的应用。提出的$\textrm{C}^2\textrm{-WORD}$算法是现有WORD方法的修改版本。我们通过在$textrm{C}^2textrm{-WORD}$中允许一个复值合并系数来扩展WORD，并通过最大白噪声增益(WNG)来寻找最优的合并系数。我们的算法提供了一个封闭的表达式来精确控制给定点的数组响应级别，从任意指定的权向量开始。此外，在不受控制的角度上，它导致较少的图案变化。详细的分析表明，所提出的$\textrm{C}^2\textrm{-WORD}$方案的性能至少与最先进的$\textrm{A}^\textrm{2}\textrm{RC}$或WORD方法一样好。通过连续应用$\textrm{C}^2\textrm{-word}$,我们提出了一种灵活有效的模式合成方法。数值算例说明了$\textrm{C}^2\textrm{-word}$在阵列响应控制和方向图综合中的灵活性和有效性。
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英文标题：
《Pattern Synthesis via Complex-Coefficient Weight Vector Orthogonal
  Decomposition--Part I: Fundamentals》
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作者：
Xuejing Zhang, Zishu He, and Xuepan Zhang
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  This paper presents a new array response control scheme named complex-coefficient weight vector orthogonal decomposition ($ \textrm{C}^2\textrm{-WORD} $) and its application to pattern synthesis. The proposed $ \textrm{C}^2\textrm{-WORD} $ algorithm is a modified version of the existing WORD approach. We extend WORD by allowing a complex-valued combining coefficient in $ \textrm{C}^2\textrm{-WORD} $, and find the optimal combining coefficient by maximizing white noise gain (WNG). Our algorithm offers a closed-from expression to precisely control the array response level of a given point starting from an arbitrarily-specified weight vector. In addition, it results less pattern variations on the uncontrolled angles. Elaborate analysis shows that the proposed $ \textrm{C}^2\textrm{-WORD} $ scheme performs at least as good as the state-of-the-art $\textrm{A}^\textrm{2}\textrm{RC} $ or WORD approach. By applying $ \textrm{C}^2\textrm{-WORD} $ successively, we present a flexible and effective approach to pattern synthesis. Numerical examples are provided to demonstrate the flexibility and effectiveness of $ \textrm{C}^2\textrm{-WORD} $ in array response control as well as pattern synthesis.
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PDF链接：
https://arxiv.org/pdf/1807.06716