摘要翻译：
本文利用矢量化运算和Kronecker积的方法，研究了噪声鲁棒归一化子带自适应滤波器（NR-NSAF）算法在均方差和均方差意义下的统计模型，包括暂态和稳态行为。该分析方法不需要高斯输入信号。此外，所提出的分析消除了在已有的子带自适应算法分析中强加于分析滤波器组的准么正假设。各种情况下的仿真结果表明了理论分析的有效性。对于算法的一种特殊形式，所提出的稳态表达式也比以往的分析具有更好的精度。
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英文标题：
《Mean-Square Performance Analysis of Noise-Robust Normalized Subband
  Adaptive Filter Algorithm》
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作者：
Yi Yu, Haiquan Zhao, Badong Chen, Wenyuan Wang, Lu Lu
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最新提交年份：
2019
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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：
  This paper studies the statistical models of the noise-robust normalized subband adaptive filter (NR-NSAF) algorithm in the mean and mean square deviation senses involving transient-state and steady-state behavior by resorting to the method of the vectorization operation and the Kronecker product. The analysis method does not require the Gaussian input signal. Moreover, the proposed analysis removes the paraunitary assumption imposed on the analysis filter banks as in the existing analyses of subband adaptive algorithms. Simulation results in various conditions demonstrate the effectiveness of our theoretical analysis. For a special form of the algorithm, the proposed steady-state expression is also better accurate than the previous analysis.
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PDF链接：
https://arxiv.org/pdf/1711.11413