• DocumentCode
    937646
  • Title

    Solution of an integral equation occurring in the theories of prediction and detection

  • Author

    Miller, K.S. ; Zadeh, L.A.

  • Volume
    2
  • Issue
    2
  • fYear
    1956
  • fDate
    6/1/1956 12:00:00 AM
  • Firstpage
    72
  • Lastpage
    75
  • Abstract
    In many of the theories of prediction and detection developed during the past decade, one encounters linear integral equations which can be subsumed under the general form \\int_a^b R(t, \\tau ) x(\\tau ) d\\tau = f(t), a \\underline\\leq t \\underline\\leq b . This equation includes as special cases the Wiener-Hopf equation and the modified Wiener-Hopf equation \\int_0^T R(\\mid t - \\tau \\mid ) x(\\tau ) d\\tau = f(t), 0 \\underline\\leq t \\underline\\leq T . The type of kernel considered in this note occurs when the noise can be regarded as the result of operating on white noise with a succession of not necessarily time-invariant linear differential and inverse-differential operators. For this type of noise, which is essentially a generalization of the stationary noise with a rational spectral density function, it is shown that the solution of the integral equation can be expressed in terms of solution of a certain linear differential equation with variable coefficients.
  • Keywords
    Integral equations; Prediction methods; Signal detection; Density functional theory; Differential equations; Filters; Gaussian noise; Integral equations; Kernel; Maximum likelihood detection; Maximum likelihood estimation; Signal detection; Signal to noise ratio; Stochastic processes; White noise;
  • fLanguage
    English
  • Journal_Title
    Information Theory, IRE Transactions on
  • Publisher
    ieee
  • ISSN
    0096-1000
  • Type

    jour

  • DOI
    10.1109/TIT.1956.1056787
  • Filename
    1056787