Spectral Analysis Techniques using Prism Signal Processing

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    4 Citations (Scopus)
    40 Downloads (Pure)


    The Prism is a new signal processing module which implements fully recursive, linear phase FIR filtering, so its computational cost is fixed irrespective of filter length. The Prism also has negligible design cost. Recent work has demonstrated how, using simple design rules, a chain of six Prisms can create a narrowband filter with arbitrary central frequency and bandwidth. In this paper, the technique is applied to spectral analysis of data, whereby a sequence of filters is applied to a data set to provide narrow frequency analysis, yielding accurate estimates of the frequencies and amplitudes of spectral peaks in the data. Although this time domain technique remains computationally expensive compared to the FFT, it can identify and reject spectral leakage, offering an alternative analysis for low amplitude and/or adjacent spectral peaks, including hidden tones, where FFT discrimination may be limited.
    Original languageEnglish
    Article number108491
    Early online date28 Sep 2020
    Publication statusPublished - Feb 2021

    Bibliographical note

    NOTICE: this is the author’s version of a work that was accepted for publication in Measurement. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Measurement, 169, (2021) DOI: 10.1016/j.measurement.2020.108491

    © 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/


    • FFT
    • Hidden tones
    • Prism signal processing
    • Spectral analysis
    • Spectral leakage

    ASJC Scopus subject areas

    • Instrumentation
    • Electrical and Electronic Engineering


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