Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-24T05:23:20.232Z Has data issue: false hasContentIssue false

High density shot noise and gaussianity

Published online by Cambridge University Press:  14 July 2016

A. Papoulis*
Affiliation:
Polytechnic Institute of Brooklyn

Abstract

The distance from Gaussianity of the shot noise process is considered, where ti are the random times of a Poisson process with average density λ(t). With F(x) the distribution function of x(t) and G(x) that of a normal process with the same mean and variance as x(t) it is shown that where If the process x(t) is stationary with λ(t) =λ and h(t, τ) = h(t – τ) and the function h(t) is bandlimited by ωc, then the above yields

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 1971 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] Blanc-Lapierre, A. and Fortet, R. (1953) Théorie des Fonctions Aléatoires. Masson et Cie, Paris.Google Scholar
[2] Rosenblatt, M. (1956) A central limit theorem and a strong mixing condition. Proc. Nat. Acad. Sci., USA 42, 4347.CrossRefGoogle Scholar
[3] Rosenblatt, M. (1961) Some comments on band-pass filters. Quart. J. Appl. Math. 18, 387393.CrossRefGoogle Scholar
[4] Papoulis, A. (1965) Probability, Random Variables, and Stochastic Processes. McGrawHill Book Co., New York.Google Scholar
[5] Doob, J. L. (1953) Stochastic Processes. John Wiley and Sons, Inc., New York.Google Scholar
[6] Papoulis, A. (1967) Limits on band-limited signals. Proc. IEEE, 55, 16771686.Google Scholar
[7] Papoulis, A. (1965) IEEE Trans. Information Theory, IT–11, 593594.Google Scholar
[8] Bergstrom, H. (1949) On the central limit theorem in the case of not equally distributed random variables. Skand. Aktuarietidsk. 32, 3762.Google Scholar
[9] Sapogov, N. A. (1959) The independent components of the sum of random variables distributed approximately normally. Vestnik Leningrad. Univ. 19, 78105.Google Scholar
[10] Mallows, C. L (1967) Linear processes are nearly Gaussian. J. Appl. Prob. 4, 313329.Google Scholar