The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform

John J Leonard - One of the best experts on this subject based on the ideXlab platform.

  • robust incremental online inference over sparse factor graphs beyond the Gaussian Case
    International Conference on Robotics and Automation, 2013
    Co-Authors: David M Rosen, Michael Kaess, John J Leonard
    Abstract:

    Many online inference problems in robotics and AI are characterized by probability distributions whose factor graph representations are sparse. While there do exist some computationally efficient algorithms (e.g. incremental smoothing and mapping (iSAM) or Robust Incremental least-Squares Estimation (RISE)) for performing online incremental maximum likelihood estimation over these models, they generally require that the distribution of interest factors as a product of Gaussians, a rather restrictive assumption. In this paper, we investigate the possibility of performing efficient incremental online estimation over sparse factor graphs in the non-Gaussian Case. Our main result is a method that generalizes iSAM and RISE by removing the assumption of Gaussian factors, thereby significantly expanding the class of distributions to which these algorithms can be applied. The generalization is achieved by means of a simple algebraic reduction that under relatively mild conditions (boundedness of each of the factors in the distribution of interest) enables an instance of the general maximum likelihood estimation problem to be reduced to an equivalent instance of least-squares minimization that can be solved efficiently online by application of iSAM or RISE. Through this construction we obtain robust, computationally efficient, and mathematically correct incremental online maximum likelihood estimators for non-Gaussian distributions over sparse factor graphs.

  • ICRA - Robust incremental online inference over sparse factor graphs: Beyond the Gaussian Case
    2013 IEEE International Conference on Robotics and Automation, 2013
    Co-Authors: David M Rosen, Michael Kaess, John J Leonard
    Abstract:

    Many online inference problems in robotics and AI are characterized by probability distributions whose factor graph representations are sparse. While there do exist some computationally efficient algorithms (e.g. incremental smoothing and mapping (iSAM) or Robust Incremental least-Squares Estimation (RISE)) for performing online incremental maximum likelihood estimation over these models, they generally require that the distribution of interest factors as a product of Gaussians, a rather restrictive assumption. In this paper, we investigate the possibility of performing efficient incremental online estimation over sparse factor graphs in the non-Gaussian Case. Our main result is a method that generalizes iSAM and RISE by removing the assumption of Gaussian factors, thereby significantly expanding the class of distributions to which these algorithms can be applied. The generalization is achieved by means of a simple algebraic reduction that under relatively mild conditions (boundedness of each of the factors in the distribution of interest) enables an instance of the general maximum likelihood estimation problem to be reduced to an equivalent instance of least-squares minimization that can be solved efficiently online by application of iSAM or RISE. Through this construction we obtain robust, computationally efficient, and mathematically correct incremental online maximum likelihood estimators for non-Gaussian distributions over sparse factor graphs.

Andrzej Królak - One of the best experts on this subject based on the ideXlab platform.

  • Gravitational-Wave Data Analysis. Formalism and Sample Applications: The Gaussian Case.
    Living reviews in relativity, 2012
    Co-Authors: Piotr Jaranowski, Andrzej Królak
    Abstract:

    The article reviews the statistical theory of signal detection in application to analysis of deterministic gravitational-wave signals in the noise of a detector. Statistical foundations for the theory of signal detection and parameter estimation are presented. Several tools needed for both theoretical evaluation of the optimal data analysis methods and for their practical implementation are introduced. They include optimal signal-to-noise ratio, Fisher matrix, false alarm and detection probabilities, [Formula: see text]-statistic, template placement, and fitting factor. These tools apply to the Case of signals buried in a stationary and Gaussian noise. Algorithms to efficiently implement the optimal data analysis techniques are discussed. Formulas are given for a general gravitational-wave signal that includes as special Cases most of the deterministic signals of interest.

  • Gravitational-Wave Data Analysis. Formalism and Sample Applications: The Gaussian Case
    Living Reviews in Relativity, 2005
    Co-Authors: Piotr Jaranowski, Andrzej Królak
    Abstract:

    The article reviews the statistical theory of signal detection in application to analysis of deterministic gravitational-wave signals in the noise of a detector. Statistical foundations for the theory of signal detection and parameter estimation are presented. Several tools needed for both theoretical evaluation of the optimal data analysis methods and for their practical implementation are introduced. They include optimal signal-to-noise ratio, Fisher matrix, false alarm and detection probabilities, ${\mathcal F}$ -statistic, template placement, and fitting factor. These tools apply to the Case of signals buried in a stationary and Gaussian noise. Algorithms to efficiently implement the optimal data analysis techniques are discussed. Formulas are given for a general gravitational-wave signal that includes as special Cases most of the deterministic signals of interest.

Kannan Ramchandran - One of the best experts on this subject based on the ideXlab platform.

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

  • robust incremental online inference over sparse factor graphs beyond the Gaussian Case
    International Conference on Robotics and Automation, 2013
    Co-Authors: David M Rosen, Michael Kaess, John J Leonard
    Abstract:

    Many online inference problems in robotics and AI are characterized by probability distributions whose factor graph representations are sparse. While there do exist some computationally efficient algorithms (e.g. incremental smoothing and mapping (iSAM) or Robust Incremental least-Squares Estimation (RISE)) for performing online incremental maximum likelihood estimation over these models, they generally require that the distribution of interest factors as a product of Gaussians, a rather restrictive assumption. In this paper, we investigate the possibility of performing efficient incremental online estimation over sparse factor graphs in the non-Gaussian Case. Our main result is a method that generalizes iSAM and RISE by removing the assumption of Gaussian factors, thereby significantly expanding the class of distributions to which these algorithms can be applied. The generalization is achieved by means of a simple algebraic reduction that under relatively mild conditions (boundedness of each of the factors in the distribution of interest) enables an instance of the general maximum likelihood estimation problem to be reduced to an equivalent instance of least-squares minimization that can be solved efficiently online by application of iSAM or RISE. Through this construction we obtain robust, computationally efficient, and mathematically correct incremental online maximum likelihood estimators for non-Gaussian distributions over sparse factor graphs.

  • ICRA - Robust incremental online inference over sparse factor graphs: Beyond the Gaussian Case
    2013 IEEE International Conference on Robotics and Automation, 2013
    Co-Authors: David M Rosen, Michael Kaess, John J Leonard
    Abstract:

    Many online inference problems in robotics and AI are characterized by probability distributions whose factor graph representations are sparse. While there do exist some computationally efficient algorithms (e.g. incremental smoothing and mapping (iSAM) or Robust Incremental least-Squares Estimation (RISE)) for performing online incremental maximum likelihood estimation over these models, they generally require that the distribution of interest factors as a product of Gaussians, a rather restrictive assumption. In this paper, we investigate the possibility of performing efficient incremental online estimation over sparse factor graphs in the non-Gaussian Case. Our main result is a method that generalizes iSAM and RISE by removing the assumption of Gaussian factors, thereby significantly expanding the class of distributions to which these algorithms can be applied. The generalization is achieved by means of a simple algebraic reduction that under relatively mild conditions (boundedness of each of the factors in the distribution of interest) enables an instance of the general maximum likelihood estimation problem to be reduced to an equivalent instance of least-squares minimization that can be solved efficiently online by application of iSAM or RISE. Through this construction we obtain robust, computationally efficient, and mathematically correct incremental online maximum likelihood estimators for non-Gaussian distributions over sparse factor graphs.

Wu-tan Chen - One of the best experts on this subject based on the ideXlab platform.

  • Maximum-likelihood blind deconvolution: non-white Bernoulli-Gaussian Case
    IEEE Transactions on Geoscience and Remote Sensing, 1991
    Co-Authors: Chong-yung Chi, Wu-tan Chen
    Abstract:

    The authors present a maximum-likelihood deconvolution (MLD) algorithm for estimating nonwhite Bernoulli-Gaussian signals mu (k), which were distorted by a linear time-invariant system upsilon (k) taking into account the measured spectrum of mu (k) such as that obtained from sonic logs. The proposed MLD algorithm can recover both the phase of a minimum-phase coloring filter upsilon /sub 1/(k) and that of upsilon (k) as long as the spectrum of mu (k) is known in advance. The authors also present some simulation results which support the proposed MLD algorithm. >

  • Maximum-Likelihood Blind Deconvolution: Non-White Bernoulli-Gaussian Case
    [Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management, 1
    Co-Authors: Chong-yung Chi, Wu-tan Chen
    Abstract:

    Todoeschuck and Jensen (1-21 recently reported that some reflectivity sequences p(k) calculated from sonic logs are not white and have a power spectral density approximately proportional to frequency, called a Joseph spectrum. The well-known MLD algorithms 7 131 can simultanmusly provide estimates of p(k), source wavelet wkh need not be minimum-phase, and statistical parameters. Although these MLD algorithms work well, they are based on the white Bernoulli-Gaussian (B-G) model for p(k). In this paper, assuming that spectrum measurements of p(k) are available, we propose a ML algorithm for blind deconvolution as p(k) is non-white with a general spectrum mywhile the spectrum of the obtained maximum-iikelihood estimate %L(k) is consistent with the measured spectrum.