Three techniques for estimating mean square values of nonstationary random processes are analyzed and compared. These include ensemble averaging, orthogonal function approximation, and short time averaging. It is shown that ensemble averaging is useful only when the number of records available is large because of the estimation errors. The orthogonal function approximation technique is shown to be better than ensemble averaging, although more difficult to mechanize. It is also shown that short time averaging generally produces biased estimates. Finally, a brief discussion is presented on the selection of the best technique to implement for particular applications.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Mean Square Measurements of Nonstationary Random Processes


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    SAE World Congress & Exhibition ; 1964



    Publication date :

    1964-01-01




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Mean and mean square measurements of nonstationary random processes

    Thrall, George P. / Bendat, Julius S. | TIBKAT | 1965




    Generating Nonstationary Random Sequences

    Mitchell, R. L. / Mcpherson, D. A. | IEEE | 1981


    Modeling Nonstationary Random Processes with an Application to Gyro Drift Rate

    Van Dierendonc, Albert J. / Brown, Robert G. | IEEE | 1969