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雷达卡
摘要翻译：
描述了一种盲源分离方法，该方法用于从假设基础源是稀疏和不相关的数据混合中提取源。所使用的方法是检测和分析一个源单独存在的时间片段。来自这些部分的信息被组合起来，以抵消实际中可能出现的噪声和信源之间的小随机相关性的影响。然后，可以使用通货紧缩方法，一次一个地使用这些综合信息来估计这些来源。概率密度函数是不假定的任何来源。将该方法与最小航向变化法、Fast-ICA和聚类PCA进行了比较。结果表明，对于本文所用的数据集，如果输入参数选择正确，所提出的方法对干净信号的处理效果最好。然而，与快速ICA和聚类方法相比，该方法的性能对这些输入参数非常敏感，对噪声也更敏感。
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英文标题：
《Extraction of Uncorrelated Sparse Sources from Signal Mixtures using a
Clustering Method》
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作者：
Malcolm Woolfson
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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：
A blind source separation method is described to extract sources from data mixtures where the underlying sources are assumed to be sparse and uncorrelated. The approach used is to detect and analyse segments of time where one source exists on its own. Information from these segments is combined to counteract the effects of noise and small random correlations between the sources that would occur in practice. This combined information can then be used to estimate the sources one at a time using a deflationary method. Probability density functions are not assumed for any of the sources. A comparison is made between the proposed method, the Minimum Heading Change method, Fast-ICA and Clusterwise PCA. It is shown, for the dataset used in this paper, that the proposed method has the best performance for clean signals if the input parameters are chosen correctly. However the performance of this method can be very sensitive to these input parameters and can also be more sensitive to noise than the Fast-ICA and Clusterwise methods.
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PDF链接：
https://arxiv.org/pdf/1802.01464
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