By Topic

Output characteristic function for an analog crosscorrelator with bandpass inputs

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)

An analysis is presented of an ideal two-channel cross-correlator in which each channel input consists of a deterministic signal combined additively with stationary Gaussian noise. It is assumed that all input quantities are bandlimited to some common passband,0 < omega_{0}- Omega/2 leq |omega | leq omega_{0} + Omega/2, with angular bandwidthOmega > 0. Moreover, the random noisesn_{1}(t)andn_{2}(t)are assumed jointly normal with crosscorrelation functionE[n_{1}(t)cdot n_{2}(t+ tau)]independent oft. After multiplication of the two composite inputs, the product process is passed through an ideal lowpass filter to produce the correlator output,G(t). The main result of the paper is an explicit determination of the characteristic function ofG(t)in closed form. This extends previous work by D. C. Cooper [1], who considered the same model with sinusoidal signals and a restricted form of dependency between the Gaussian noises in the two channels. The more general derivation given here makes use of canonical representations for the bandpass input quantities and exhibits the system output as a quadratic form in the (Gaussian) quadrature components. A recent result of Yu. S. Lezin [6] on the output probability density of an autocorrelator with bandpass inputs is shown to be a special case of the analysis.

Published in:

Information Theory, IEEE Transactions on  (Volume:13 ,  Issue: 1 )