摘要翻译：
近年来，高阶统计量(HOS)和基于稀疏性的阵列是波达方向估计技术中讨论最多的两种方法。它们不仅为处理欠定情况提供了增强的自由度，而且提高了系统的估计精度。为了在有限的传感器数目下实现高精度和更多自由度，本文提出了一种基于四阶统计量的方法。使用N个物理传感器，虚拟阵列的孔径为O(16N^4)。该方法可推广到HOS系统，使其自由度提高了许多倍。数值仿真结果表明，该方法在提高分辨能力的同时，最大限度地提高了前人提出的方法的自由度。
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英文标题：
《Enhanced Array Aperture using Higher Order Statistics for DoA Estimation》
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作者：
Payal Gupta and Monika Agrawal
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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：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  Recently, the higher order statistics (HOS) and sparsity based array are most talked about techniques to estimate the Direction of Arrival (DoA). They not only provide enhanced Degree of Freedom (DoF) to handle underdetermined cases but also improve the estimation accuracy of the system. To achieve high accuracy and more number of DoF with limited number of sensors, here we have proposed a method based on the fourth order statistics. The aperture of virtual array becomes O(16N^4) using N physical sensors. Proposed method can be extended to the HOS which increases the DoF by many folds. Numeric simulation validates these claims that the proposed method increases the resolution capacity as well as maximize the DoF among all the earlier proposed method.
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PDF链接：
https://arxiv.org/pdf/1711.05923