发帖
雷达卡
摘要翻译：
我们重点研究了一种无对准的方法来从大量噪声随机移位的观测中估计潜在信号。具体地说，我们从观测中估计信号的均值、功率谱和双谱。由于双谱包含信号的相位信息，可靠的双谱反演算法在许多应用中都是有用的。针对这一问题，我们提出了一种基于归一化双谱矩阵谱分解的新算法。对于干净信号，我们证明了归一化双谱矩阵的特征向量对应于信号及其移位拷贝的真相位。此外，谱方法对噪声具有较强的鲁棒性。它可以作为双谱反演局部非凸优化的一种稳定有效的初始化技术。
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英文标题：
《A Spectral Method for Stable Bispectrum Inversion with Application to
Multireference Alignment》
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作者：
Hua Chen, Mona Zehni, and Zhizhen Zhao
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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：
We focus on an alignment-free method to estimate the underlying signal from a large number of noisy randomly shifted observations. Specifically, we estimate the mean, power spectrum, and bispectrum of the signal from the observations. Since bispectrum contains the phase information of the signal, reliable algorithms for bispectrum inversion is useful in many applications. We propose a new algorithm using spectral decomposition of the normalized bispectrum matrix for this task. For clean signals, we show that the eigenvectors of the normalized bispectrum matrix correspond to the true phases of the signal and its shifted copies. In addition, the spectral method is robust to noise. It can be used as a stable and efficient initialization technique for local non-convex optimization for bispectrum inversion.
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PDF链接：
https://arxiv.org/pdf/1802.10493
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