This chapter presents the modeling of linear dynamic systems excited by random inputs, called noise. Continuous‐time systems and discrete‐time systems are discussed. The state‐space models for continuous time and discrete time are presented, and it is shown how the latter can be derived from the former by discretization. The state‐space model directly defined in discrete time is also discussed. The Markov property of the state of a linear system driven by white noise is discussed and used to obtain the propagation equations for the mean and covariance of the state. The power spectral density (the Fourier transform of the autocorrelation function) of the output of a linear system is related to the state space representation via the transfer function, and it is shown how its factorization makes it possible to prewhiten an autocorrelated random process or sequence. A problem solving section appears at the end of the chapter.


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    Titel :

    Linear Dynamic Systems with Random Inputs


    Beteiligte:


    Erscheinungsdatum :

    2002-01-04


    Format / Umfang :

    20 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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