MVDR波束形成器的单位圆整流

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摘要翻译：
样本矩阵反演(SMI)波束形成器利用样本协方差矩阵(SCM)实现Capon最小方差无失真(MVDR)波束形成。在快照受限的环境中，SCM的条件很差，导致SMI波束形成器的性能次优。对SCM估计施加结构约束以满足集成MVDR波束形成器的已知理论特性，从而减轻了有限快照对SMI波束形成器性能的影响。Toeplitz校正和权向量范数定界是解决这类约束的常用方法。本文提出了一种单位圆整流技术，它约束SMI波束形成器满足集成MVDR波束形成器的一个性质：对于均匀线阵上的窄带平面波波束形成，MVDR权阵多项式的零点必须落在单位圆上。数值仿真结果表明，与经典SMI波束形成器相比，所得到的单位圆MVDR（UC MVDR）波束形成器在抑制离散干扰和白背景噪声方面有明显的改善。此外，UC MVDR波束形成器比对角加载的MVDR波束形成器更能抑制离散干扰，从而使信干噪比最大化。
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英文标题：
《Unit circle rectification of the MVDR beamformer》
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作者：
Saurav R Tuladhar and John R Buck
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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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一级分类：Computer Science        计算机科学
二级分类：Systems and Control        系统与控制
分类描述：cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
cs.sy是eess.sy的别名。本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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英文摘要：
  The sample matrix inversion (SMI) beamformer implements Capon's minimum variance distortionless (MVDR) beamforming using the sample covariance matrix (SCM). In a snapshot limited environment, the SCM is poorly conditioned resulting in a suboptimal performance from the SMI beamformer. Imposing structural constraints on the SCM estimate to satisfy known theoretical properties of the ensemble MVDR beamformer mitigates the impact of limited snapshots on the SMI beamformer performance. Toeplitz rectification and bounding the norm of weight vector are common approaches for such constrains. This paper proposes the unit circle rectification technique which constraints the SMI beamformer to satisfy a property of the ensemble MVDR beamformer: for narrowband planewave beamforming on a uniform linear array, the zeros of the MVDR weight array polynomial must fall on the unit circle. Numerical simulations show that the resulting unit circle MVDR (UC MVDR) beamformer frequently improves the suppression of both discrete interferers and white background noise compared to the classic SMI beamformer. Moreover, the UC MVDR beamformer is shown to suppress discrete interferers better than the MVDR beamformer diagonally loaded to maximize the SINR.
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PDF链接：
https://arxiv.org/pdf/1807.01237

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关键词：单位圆 Optimization Applications Experimental Application 理论 Capon 矩阵 satisfy 向量