摘要翻译：
经典的谱分析方法利用窗函数来减少强谱分量对弱谱分量的掩蔽效应。降低旁瓣的主要代价是降低输出频谱的信噪比(SNR)水平。我们提出了一种单次快照方法，它优化了在有限的候选窗口集合中为每个光谱单元选择最合适的窗口函数，例如矩形窗口、Hamming窗口、Blackman窗口。主要目标是根据在该频谱仓处遇到的干扰电平，在每个频谱输出处利用不同的窗函数，以减少与加窗操作相关联的信噪比损失。换句话说，具有强干扰抑制能力的窗口仅当存在足够强的干扰源破坏感兴趣的光谱仓时才使用，即仅当需要该窗口时才使用。所实现的窗口信噪比损失的减少对于低信噪比目标的检测是重要的。
---
英文标题：
《An Automated Window Selection Procedure For DFT Based Detection Schemes
  To Reduce Windowing SNR Loss》
---
作者：
Cagatay Candan
---
最新提交年份：
2017
---
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。
--

---
英文摘要：
  The classical spectrum analysis methods utilize window functions to reduce the masking effect of a strong spectral component over weaker components. The main cost of side-lobe reduction is the reduction of signal-to-noise ratio (SNR) level of the output spectrum. We present a single snapshot method which optimizes the selection of most suitable window function among a finite set of candidate windows, say rectangle, Hamming, Blackman windows, for each spectral bin. The main goal is to utilize different window functions at each spectral output depending on the interference level encountered at that spectral bin so as to reduce the SNR loss associated with the windowing operation. Stated differently, the windows with strong interference suppression capabilities are utilized only when a sufficiently powerful interferer is corrupting the spectral bin of interest is present, i.e. only when this window is needed. The achieved reduction in the windowing SNR loss can be important for the detection of low SNR targets.
---
PDF链接：
https://arxiv.org/pdf/1710.102