摘要翻译：
为了解决许多具有多复数极点线性低通传输特性的物理和电气系统的波形畸变和信号延迟问题，提出了一种基于通用数字信号处理(DSP)的从畸变输出波形中实时恢复原始源波形的方法。从具有任意分母多项式的多极子低通传递函数的卷积核表示出发，证明了即使部分或全部极点是复杂的，只要采用特定的移动平均算法，只需用实数DSP计算，就可以实时准确地恢复源波形。所提出的数字信号恢复方法具有直流精度，不受初始条件、瞬态信号和谐振幅度增强的影响。数据恢复的噪声特性与低通滤波器特性相反。该方法适用于大多数传感器和放大器在接近其频率响应极限或谐振频率附近工作，以准确地解卷多极特性，改善数据采集系统和数字反馈控制系统的整体性能。
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英文标题：
《Real-time digital signal recovery for a low-pass transfer function
  system with multiple complex poles》
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作者：
Jhinhwan Lee
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最新提交年份：
2018
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分类信息：

一级分类：Physics        物理学
二级分类：Instrumentation and Detectors        仪器仪表和探测器
分类描述：Instrumentation and Detectors for research in natural science, including optical, molecular, atomic, nuclear and particle physics instrumentation and the associated electronics, services, infrastructure and control equipment.
用于自然科学研究的仪器和探测器，包括光学、分子、原子、核和粒子物理仪器和相关的电子学、服务、基础设施和控制设备。
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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 order to solve the problems of waveform distortion and signal delay by many physical and electrical systems with linear low-pass transfer characteristics with multiple complex poles, a general digital-signal-processing (DSP)-based method of real-time recovery of the original source waveform from the distorted output waveform is proposed. From the convolution kernel representation of a multiple-pole low-pass transfer function with an arbitrary denominator polynomial with real valued coefficients, it is shown that the source waveform can be accurately recovered in real time using a particular moving average algorithm with real-valued DSP computations only, even though some or all of the poles are complex. The proposed digital signal recovery method is DC-accurate and unaffected by initial conditions, transient signals, and resonant amplitude enhancement. The noise characteristics of the data recovery shows inverse of the low-pass filter characteristics. This method can be applied to most sensors and amplifiers operating close to their frequency response limits or around their resonance frequencies to accurately deconvolute the multiple-pole characteristics and to improve the overall performances of data acquisition systems and digital feedback control systems.
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PDF链接：
https://arxiv.org/pdf/1807.07105