Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Real-time digital signal recovery for a low-pass transfer function system with multiple complex poles

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. Preprints 2018, 2018070388 (doi: 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 (doi: 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 & Electronic Engineering

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