发帖
雷达卡
摘要翻译：
在实际的多天线雷达中，天线的缺陷会降低系统的性能。研究了天线互耦效应未知的多输入多输出(MIMO)雷达系统的波达方向(DOA)估计问题。为了充分利用目标在空间域的稀疏性，基于压缩感知(CS)的检测方法通过离散化检测区域和构造字典矩阵来实现。与现有的DOA估计方法不同，本文同时考虑了稀疏采样引起的离网间隙和未知的天线间互耦效应，建立了一种新的稀疏系统DOA估计模型。然后，提出了一种新的基于稀疏贝叶斯学习(SBL)的方法--互耦稀疏贝叶斯学习(SBLMC)。该方法基于期望极大值(EM)估计所有未知参数，包括噪声方差、互耦向量、离网向量和散射系数方差向量。此外，还从理论上推导了所有未知参数的先验分布。对于互耦效应未知的MIMO雷达，在保持可接受的计算复杂度的情况下，本文提出的SBLMC方法在DOA估计性能上优于现有的方法。
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英文标题：
《Off-Grid DOA Estimation Using Sparse Bayesian Learning in MIMO Radar
With Unknown Mutual Coupling》
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作者：
Peng Chen and Zhenxin Cao and Zhimin Chen and Xianbin Wang
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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：
In the practical radar with multiple antennas, the antenna imperfections degrade the system performance. In this paper, the problem of estimating the direction of arrival (DOA) in multiple-input and multiple-output (MIMO) radar system with unknown mutual coupling effect between antennas is investigated. To exploit the target sparsity in the spatial domain, the compressed sensing (CS)-based methods have been proposed by discretizing the detection area and formulating the dictionary matrix, so an \emph{off-grid} gap is caused by the discretization processes. In this paper, different from the present DOA estimation methods, both the off-grid gap due to the sparse sampling and the unknown mutual coupling effect between antennas are considered at the same time, and a novel sparse system model for DOA estimation is formulated. Then, a novel sparse Bayesian learning (SBL)-based method named sparse Bayesian learning with the mutual coupling (SBLMC) is proposed, where an expectation-maximum (EM)-based method is established to estimate all the unknown parameters including the noise variance, the mutual coupling vectors, the off-grid vector and the variance vector of scattering coefficients. Additionally, the prior distributions for all the unknown parameters are theoretically derived. With regard to the DOA estimation performance, the proposed SBLMC method can outperform state-of-the-art methods in the MIMO radar with unknown mutual coupling effect, while keeping the acceptable computational complexity.
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PDF链接：
https://arxiv.org/pdf/1804.0446
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