Version 1
: Received: 20 July 2018 / Approved: 20 July 2018 / Online: 20 July 2018 (14:37:40 CEST)
Version 2
: Received: 15 August 2018 / Approved: 15 August 2018 / Online: 15 August 2018 (14:26:50 CEST)
How to cite:
Lee, J. Real-time digital signal recovery for a low-pass transfer function system with multiple complex poles. Preprints2018, 2018070388. https://doi.org/10.20944/preprints201807.0388.v1
Lee, J. Real-time digital signal recovery for a low-pass transfer function system with multiple complex poles. Preprints 2018, 2018070388. https://doi.org/10.20944/preprints201807.0388.v1
Lee, J. Real-time digital signal recovery for a low-pass transfer function system with multiple complex poles. Preprints2018, 2018070388. https://doi.org/10.20944/preprints201807.0388.v1
APA Style
Lee, J. (2018). Real-time digital signal recovery for a low-pass transfer function system with multiple complex poles. Preprints. https://doi.org/10.20944/preprints201807.0388.v1
Chicago/Turabian Style
Lee, J. 2018 "Real-time digital signal recovery for a low-pass transfer function system with multiple complex poles" Preprints. https://doi.org/10.20944/preprints201807.0388.v1
Abstract
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.
Keywords
signal recovery, deconvolution, transfer function, digital signal processing
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
