By Topic

On evaluating polarity-coincidence correlation when the two inputs are statistically dependent (Corresp.)

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
$33 $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)

Polarity-coincidence correlation (pcc) is usually analyzed under the assumption of independent noise inputs and small input-signal power. Thus the difficulty of evaluating the variance of the PCC statistic for inputs with arbitrary cross correlations is avoided. An expression for the variance that can be conveniently evaluated on a computer is discussed. As one example, the PCC statistic is analyzed for a strong Markovian signal that is added to two independent, white noise inputs. The effect of hard limiting is determined as a function of the input signal-to-noise ratio. In another example, the PCC detector is analyzed for a small Markovian signal that is added to two dependent Markov noise inputs. In this case, the cost of clipping increases substantially with the noise correlation.

Published in:

IEEE Transactions on Information Theory  (Volume:29 ,  Issue: 2 )