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摘要翻译：
本文研究了亚奈奎斯特采样框架下的宽带频谱感知和波达方向(DoA)联合估计问题。具体来说，考虑到几个不相关的窄带信号在一个很宽（比如几GHz）的频带上传播的情况，我们的目标是估计与窄带源相关的载波频率和DOA，以及重建这些窄带信号的功率谱。为了克服宽带频谱感知中的采样率瓶颈，我们提出了一种新的基于相控阵的变时延亚奈奎斯特采样结构，该结构采用均匀线阵(ULA),每个天线的接收信号被延迟一个可变的时间量，然后由同步的低速率模数转换器(ADC)进行采样。基于采集的亚奈奎斯特样本，我们计算了一组具有不同时滞的互相关矩阵，并提出了一种基于CandeComp/Parafac(CP)分解的联合DoA、载频和功率谱恢复方法。分析了相关参数和功率谱的理想恢复条件。我们的分析表明，我们提出的方法不需要对宽带频谱施加任何稀疏约束，只需要采样率大于所有源中带宽最大的窄带源信号的带宽。仿真结果表明，该方法只需少量数据样本，就能获得接近相关CRM{e}r-Rao界的估计精度。
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英文标题：
《Phased Array-Based Sub-Nyquist Sampling for Joint Wideband Spectrum
Sensing and Direction-of-Arrival Estimation》
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作者：
Feiyu Wang, Jun Fang, Huiping Duan and Hongbin Li
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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, we study the problem of joint wideband spectrum sensing and direction-of-arrival (DoA) estimation in a sub-Nyquist sampling framework. Specifically, considering a scenario where a few uncorrelated narrowband signals spread over a wide (say, several GHz) frequency band, our objective is to estimate the carrier frequencies and the DoAs associated with the narrowband sources, as well as reconstruct the power spectra of these narrowband signals. To overcome the sampling rate bottleneck for wideband spectrum sensing, we propose a new phased-array based sub-Nyquist sampling architecture with variable time delays, where a uniform linear array (ULA) is employed and the received signal at each antenna is delayed by a variable amount of time and then sampled by a synchronized low-rate analog-digital converter (ADC). Based on the collected sub-Nyquist samples, we calculate a set of cross-correlation matrices with different time lags, and develop a CANDECOMP/PARAFAC (CP) decomposition-based method for joint DoA, carrier frequency and power spectrum recovery. Perfect recovery conditions for the associated parameters and the power spectrum are analyzed. Our analysis reveals that our proposed method does not require to place any sparse constraint on the wideband spectrum, only needs the sampling rate to be greater than the bandwidth of the narrowband source signal with the largest bandwidth among all sources. Simulation results show that our proposed method can achieve an estimation accuracy close to the associated Cram\'{e}r-Rao bounds (CRBs) using only a small number of data samples.
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PDF链接：
https://arxiv.org/pdf/1710.00773
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