[电气工程与系统科学] 三维非圆信号的DOA和极化估计 毫米波极化Massive MIMO系统 [推广有奖]

摘要翻译：
针对毫米波极化大规模/大规模多输入多输出(MIMO)系统中二维非圆信号的波达方向(DOA)和极化估计问题，提出了一种多信号分类(MUSIC)算法。传统的基于MUSIC算法可以对圆形信号进行DOA和极化估计，也可以对非圆形信号进行DOA估计。相比之下，在该算法中，只有DOA估计需要频谱搜索，而极化估计有一个封闭的表达式。首先，提出了一种新的降维MUSIC(DRMUSIC)算法，用于圆信号的DOA和极化估计，具有较低的计算复杂度。其次，基于四元数理论，提出了一种新的非圆信号参数估计算法--四元数非圆MUSIC（QNC-MUSIC）。然后根据QNC-MUSIC的DOA估计结果，利用DR-MUSIC中极化估计的闭式表达式，得到非圆信号的极化估计。此外，计算复杂度分析表明，与传统的DOA和极化估计算法相比，本文提出的QNC-MUSIC和DRMUSIC算法具有更低的计算复杂度，尤其是在信源数目较大的情况下。推导了非圆信号二维DOA和极化参数估计的随机Cramer-Rao界(CRB)。最后给出的数值算例表明，在大规模/海量MIMO系统中，所提出的算法可以提高参数估计性能。
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英文标题：
《DOA and Polarization Estimation for Non-Circular Signals in 3-D
  Millimeter Wave Polarized Massive MIMO Systems》
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作者：
Liangtian Wan, Kaihui Liu, Ying-Chang Liang, Tong Zhu
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最新提交年份：
2017
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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：
  In this paper, an algorithm of multiple signal classification (MUSIC) is proposed for two-dimensional (2-D) direction of- arrival (DOA) and polarization estimation of non-circular signal in three-dimensional (3-D) millimeter wave polarized largescale/ massive multiple-input-multiple-output (MIMO) systems. The traditional MUSIC-based algorithms can estimate either the DOA and polarization for circular signal or the DOA for non-circular signal by using spectrum search. By contrast, in the proposed algorithm only the DOA estimation needs spectrum search, and the polarization estimation has a closedform expression. First, a novel dimension-reduced MUSIC (DRMUSIC) is proposed for DOA and polarization estimation of circular signal with low computational complexity. Next, based on the quaternion theory, a novel algorithm named quaternion non-circular MUSIC (QNC-MUSIC) is proposed for parameter estimation of non-circular signal with high estimation accuracy. Then based on the DOA estimation result using QNC-MUSIC, the polarization estimation of non-circular signal is acquired by using the closed-form expression of the polarization estimation in DR-MUSIC. In addition, the computational complexity analysis shows that compared with the conventional DOA and polarization estimation algorithms, our proposed QNC-MUSIC and DRMUSIC have much lower computational complexity, especially when the source number is large. The stochastic Cramer-Rao Bound (CRB) for the estimation of the 2-D DOA and polarization parameters of the non-circular signals is derived as well. Finally, numerical examples are provided to demonstrate that the proposed algorithms can improve the parameter estimation performance when the large-scale/massive MIMO systems are employed.
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PDF链接：
https://arxiv.org/pdf/1712.05587