Estimation of Correlation Functions by the Random Decrement Technique

Rune Brincker, Steen Krenk, Jacob Laigaard Jensen

    Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

    15 Citations (Scopus)


    The Random Decrement (RDD) Technique is a versatile technique for characterization of random signals in the time domain. In this paper a short review of the theoretical basis is given, and the technique is illustrated by estimating auto-correlation functions and cross-correlation functions on modal responses simulated by two SDOF ARMA models loaded by the same band-limited white noise. The speed and the accuracy of the RDD technique is compared to the Fast Fourier Transform (FFT) technique. The RDD technique does not involve multiplications, but only additions. Therefore, the technique is very fast - in some cases up to 100 times faster than the FFT technique. Another important advantage is that if the RDD technique is implemented correctly, the correlation function estimates are unbiased. Comparison with exact solutions for the correlation functions shows that the RDD auto-correlation estimates suffer from smaller RDD auto-correlation estimation errors than the corresponding FFT estimates. However, in the case of estimating cross-correlation functions for the stochastic processes with low mutual correlation, the FFT tehcnique might be more accurate.
    Original languageEnglish
    Title of host publicationProceedings of the Florence Modal Analysis Conference
    Number of pages6
    PublisherDipartimento di Meccanica e Technologie Industriali
    Publication date1991
    Publication statusPublished - 1991
    EventFlorence Modal Analysis Conference - Firenze, Italy
    Duration: 10 Sep 199112 Sep 1991


    ConferenceFlorence Modal Analysis Conference


    • Random Decrement (RDD) Technique
    • Characterization of random signals
    • Estimating auto-correlation functions
    • Estimating cross-correlation fuctions
    • Fast Fourier Transform (FFT)


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