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Ram Zamir - One of the best experts on this subject based on the ideXlab platform.

  • A proof of the Fisher Information Inequality via a data processing argument
    IEEE Transactions on Information Theory, 1998
    Co-Authors: Ram Zamir
    Abstract:

    The Fisher Information J(X) of a random variable X under a translation parameter appears in Information theory in the classical proof of the entropy-power Inequality (EPI). It enters the proof of the EPI via the De-Bruijn identity, where it measures the variation of the differential entropy under a Gaussian perturbation, and via the convolution Inequality J(X+Y)/sup -1//spl ges/J(X)/sup -1/+J(Y)/sup -1/ (for independent X and Y), known as the Fisher Information Inequality (FII). The FII is proved in the literature directly, in a rather involved way. We give an alternative derivation of the FII, as a simple consequence of a "data processing Inequality" for the Cramer-Rao lower bound on parameter estimation.

  • A generalization of the entropy power Inequality with applications
    IEEE Transactions on Information Theory, 1993
    Co-Authors: Ram Zamir, M. Feder
    Abstract:

    The authors prove the following generalization of the entropy power Inequality: h(ax)>or=h(Ax) where h(.) denotes (joint-) differential-entropy x=x/sub 1/...x/sub n/, is a random vector with independent components, x=x...x/sub n/, is a Gaussian vector with independent components such that h(x/sub i/)=h(x/sub i/), i=1...n, and A is any matrix. This generalization of the entropy-power Inequality is applied to show that a non-Gaussian vector with independent components becomes "closer" to Gaussianity after a linear transformation, where the distance to Gaussianity is measured by the Information divergence. Another application is a lower bound, greater than zero, for the mutual-Information between nonoverlapping spectral components of a non-Gaussian white process. They also describe a dual generalization of the Fisher Information Inequality.

M. Feder - One of the best experts on this subject based on the ideXlab platform.

  • A generalization of the entropy power Inequality with applications
    IEEE Transactions on Information Theory, 1993
    Co-Authors: Ram Zamir, M. Feder
    Abstract:

    The authors prove the following generalization of the entropy power Inequality: h(ax)>or=h(Ax) where h(.) denotes (joint-) differential-entropy x=x/sub 1/...x/sub n/, is a random vector with independent components, x=x...x/sub n/, is a Gaussian vector with independent components such that h(x/sub i/)=h(x/sub i/), i=1...n, and A is any matrix. This generalization of the entropy-power Inequality is applied to show that a non-Gaussian vector with independent components becomes "closer" to Gaussianity after a linear transformation, where the distance to Gaussianity is measured by the Information divergence. Another application is a lower bound, greater than zero, for the mutual-Information between nonoverlapping spectral components of a non-Gaussian white process. They also describe a dual generalization of the Fisher Information Inequality.

O. Johnson - One of the best experts on this subject based on the ideXlab platform.

Raymond W Yeung - One of the best experts on this subject based on the ideXlab platform.

  • on a relation between Information inequalities and group theory
    IEEE Transactions on Information Theory, 2002
    Co-Authors: Terence H Chan, Raymond W Yeung
    Abstract:

    We establish a one-to-one correspondence between Information inequalities and group inequalities. The major implication of our result is that we can prove Information inequalities by proving the corresponding group inequalities, and vice versa. By giving a group-theoretic proof for all Shannon-type inequalities, we suggest that new inequalities could be discovered by making use of the rich set of tools in group theory. On the other hand, via a non-Shannon-type Information Inequality discovered by Zhang and Yeung (1997), we obtain a new Inequality in group theory whose meaning is yet to be understood.

Yuan Shen - One of the best experts on this subject based on the ideXlab platform.

  • Fundamental Limits of Wideband Cooperative Localization via Fisher Information
    2007 IEEE Wireless Communications and Networking Conference, 2007
    Co-Authors: Yuan Shen, Henk Wymeersch
    Abstract:

    Determination of position accuracy for geolocation is a fundamental issue in wireless sensor networks. In a dense obstacle environment, anchors (or base stations) may not be able to provide sufficient localization Information to agents because of radio blockage or limited range. In such cases, cooperation among agents (or mobile stations) can be very helpful. In this paper, we develop a model for cooperative localization based on time-of-arrival (TOA) ranging Information and derive the position error bound (PEB) for agents in the network using Information Inequality. Equivalent Fisher Information (EFI), which has been applied in the single agent localization case (Sen and Win, 2007), is employed to characterize the localization accuracy. From analysis, we also show that anchors and agents are essentially equivalent in our unified cooperative localization model.

  • Fundamental Limits of Wideband Localization Accuracy via Fisher Information
    2007 IEEE Wireless Communications and Networking Conference, 2007
    Co-Authors: Yuan Shen
    Abstract:

    Determination of position accuracy for geolocation is a fundamental issue in wireless sensor networks. This paper derives the position error bound (PEB), a fundamental limit for localization accuracy, by using Information Inequality. In particular, the authors consider all multipath propagation parameters, and hence our bound is tighter than those of previous work. To alleviate computation complexity, the authors put forth the notion of equivalent Fisher Information (EFI) to characterize the localization accuracy. This approach also unifies the contributions from line-of-sight (LOS), non-line-of-sight (NLOS), and a priori knowledge to the PEB in a consistent form. These results are applicable to ultra-wide bandwidth (UWB) systems as a specific case.