The paper obtains the general form of the cross-covariance function of vector fractional Brownian motion with correlated components having different self-similarity indices.

Abstract: In this note, we establish an asymptotic expansion for the centering parameter appearing in the central limit theorems for linear spectral statistic of large-dimensional sample covariance matrices when the population has a spiked covariance structure. As an application, we provide an asymptotic power function for the corrected likelihood ratio statistic for testing the presence of spi...

We investigated the meridional circulation of photospheric faculae throughout cycle 19. Together with the rotation observed in a companion paper (Meunier et al. 1996), we were able to address the problem of the existence of the observed differential rotation. Themeridional circulation is characterized by a strong north-south asymmetry. The distribution of meridional circulation around the mean ...

We describe a method for causal inference that measures the strength of statistical dependence by the Hilbert-Schmidt norm of kernelbased conditional cross-covariance operators. We consider the increase of the dependence of two variables X and Y by conditioning on a third variable Z as a hint for Z being a common effect of X and Y . Based on this assumption, we collect “votes” for hypothetical ...

In cooperative localization (CL), communicating mobile agents use inter-agent relative measurements to improve their dead-reckoning-based global localization. Measurement scheduling enables an agent decide which subset of available it should process when its computational resources are limited. Optimal measurement is NP-hard combinatorial optimization problem. The so-called sequential greedy (S...

Gibbs Sampling, a Markov Chain Monte Carlo technique, has been shown to be a powerful tool for geoacoustic inversion and source localization. By providing estimates of posterior joint distributions, it offers a global optimization route for multi-dimensional matched-field processing that reports uncertainty and covariance in addition to point estimates. In this work, Gibbs Sampling is applied t...

Acoustic source localization is classically addressed with Matched-field processing (MFP). However, since MFP is necessarily based on accurately modeled ocean environment, it may not be well adapted as realistic ocean environment is far more complex and unpredictable. In this paper, the potential of applying machine learning techniques is explored regarding the source localization with unstable...

The computational bottleneck in all information-based algorithms for simultaneous localization and mapping (SLAM) is the recovery of the state mean and covariance. The mean is needed to evaluate model Jacobians and the covariance is needed to generate data association hypotheses. In general, recovering the state mean and covariance requires the inversion of a matrix with the size of the state, ...