19,275 research outputs found
Spherical Slepian functions and the polar gap in geodesy
The estimation of potential fields such as the gravitational or magnetic
potential at the surface of a spherical planet from noisy observations taken at
an altitude over an incomplete portion of the globe is a classic example of an
ill-posed inverse problem. Here we show that the geodetic estimation problem
has deep-seated connections to Slepian's spatiospectral localization problem on
the sphere, which amounts to finding bandlimited spherical functions whose
energy is optimally concentrated in some closed portion of the unit sphere.
This allows us to formulate an alternative solution to the traditional damped
least-squares spherical harmonic approach in geodesy, whereby the source field
is now expanded in a truncated Slepian function basis set. We discuss the
relative performance of both methods with regard to standard statistical
measures as bias, variance and mean-square error, and pay special attention to
the algorithmic efficiency of computing the Slepian functions on the region
complementary to the axisymmetric polar gap characteristic of satellite
surveys. The ease, speed, and accuracy of this new method makes the use of
spherical Slepian functions in earth and planetary geodesy practical.Comment: 14 figures, submitted to the Geophysical Journal Internationa
On determining the number of spikes in a high-dimensional spiked population model
In a spiked population model, the population covariance matrix has all its
eigenvalues equal to units except for a few fixed eigenvalues (spikes).
Determining the number of spikes is a fundamental problem which appears in many
scientific fields, including signal processing (linear mixture model) or
economics (factor model). Several recent papers studied the asymptotic behavior
of the eigenvalues of the sample covariance matrix (sample eigenvalues) when
the dimension of the observations and the sample size both grow to infinity so
that their ratio converges to a positive constant. Using these results, we
propose a new estimator based on the difference between two consecutive sample
eigenvalues
Source Coding in Networks with Covariance Distortion Constraints
We consider a source coding problem with a network scenario in mind, and
formulate it as a remote vector Gaussian Wyner-Ziv problem under covariance
matrix distortions. We define a notion of minimum for two positive-definite
matrices based on which we derive an explicit formula for the rate-distortion
function (RDF). We then study the special cases and applications of this
result. We show that two well-studied source coding problems, i.e. remote
vector Gaussian Wyner-Ziv problems with mean-squared error and mutual
information constraints are in fact special cases of our results. Finally, we
apply our results to a joint source coding and denoising problem. We consider a
network with a centralized topology and a given weighted sum-rate constraint,
where the received signals at the center are to be fused to maximize the output
SNR while enforcing no linear distortion. We show that one can design the
distortion matrices at the nodes in order to maximize the output SNR at the
fusion center. We thereby bridge between denoising and source coding within
this setup
Slepian functions and their use in signal estimation and spectral analysis
It is a well-known fact that mathematical functions that are timelimited (or
spacelimited) cannot be simultaneously bandlimited (in frequency). Yet the
finite precision of measurement and computation unavoidably bandlimits our
observation and modeling scientific data, and we often only have access to, or
are only interested in, a study area that is temporally or spatially bounded.
In the geosciences we may be interested in spectrally modeling a time series
defined only on a certain interval, or we may want to characterize a specific
geographical area observed using an effectively bandlimited measurement device.
It is clear that analyzing and representing scientific data of this kind will
be facilitated if a basis of functions can be found that are "spatiospectrally"
concentrated, i.e. "localized" in both domains at the same time. Here, we give
a theoretical overview of one particular approach to this "concentration"
problem, as originally proposed for time series by Slepian and coworkers, in
the 1960s. We show how this framework leads to practical algorithms and
statistically performant methods for the analysis of signals and their power
spectra in one and two dimensions, and on the surface of a sphere.Comment: Submitted to the Handbook of Geomathematics, edited by Willi Freeden,
Zuhair M. Nashed and Thomas Sonar, and to be published by Springer Verla
Scalar and vector Slepian functions, spherical signal estimation and spectral analysis
It is a well-known fact that mathematical functions that are timelimited (or
spacelimited) cannot be simultaneously bandlimited (in frequency). Yet the
finite precision of measurement and computation unavoidably bandlimits our
observation and modeling scientific data, and we often only have access to, or
are only interested in, a study area that is temporally or spatially bounded.
In the geosciences we may be interested in spectrally modeling a time series
defined only on a certain interval, or we may want to characterize a specific
geographical area observed using an effectively bandlimited measurement device.
It is clear that analyzing and representing scientific data of this kind will
be facilitated if a basis of functions can be found that are "spatiospectrally"
concentrated, i.e. "localized" in both domains at the same time. Here, we give
a theoretical overview of one particular approach to this "concentration"
problem, as originally proposed for time series by Slepian and coworkers, in
the 1960s. We show how this framework leads to practical algorithms and
statistically performant methods for the analysis of signals and their power
spectra in one and two dimensions, and, particularly for applications in the
geosciences, for scalar and vectorial signals defined on the surface of a unit
sphere.Comment: Submitted to the 2nd Edition of the Handbook of Geomathematics,
edited by Willi Freeden, Zuhair M. Nashed and Thomas Sonar, and to be
published by Springer Verlag. This is a slightly modified but expanded
version of the paper arxiv:0909.5368 that appeared in the 1st Edition of the
Handbook, when it was called: Slepian functions and their use in signal
estimation and spectral analysi
Errors on the inverse problem solution for a noisy spherical gravitational wave antenna
A single spherical antenna is capable of measuring the direction and
polarization of a gravitational wave. It is possible to solve the inverse
problem using only linear algebra even in the presence of noise. The simplicity
of this solution enables one to explore the error on the solution using
standard techniques. In this paper we derive the error on the direction and
polarization measurements of a gravitational wave. We show that the solid angle
error and the uncertainty on the wave amplitude are direction independent. We
also discuss the possibility of determining the polarization amplitudes with
isotropic sensitivity for any given gravitational wave source.Comment: 13 pages, 4 figures, LaTeX2e, IOP style, submitted to CQ
Robust equalization of multichannel acoustic systems
In most real-world acoustical scenarios, speech signals captured by distant microphones from a source are reverberated due to multipath propagation, and the reverberation may impair speech intelligibility. Speech dereverberation can be achieved
by equalizing the channels from the source to microphones. Equalization systems can
be computed using estimates of multichannel acoustic impulse responses. However,
the estimates obtained from system identification always include errors; the fact that
an equalization system is able to equalize the estimated multichannel acoustic system does not mean that it is able to equalize the true system. The objective of this
thesis is to propose and investigate robust equalization methods for multichannel
acoustic systems in the presence of system identification errors.
Equalization systems can be computed using the multiple-input/output inverse theorem or multichannel least-squares method. However, equalization systems
obtained from these methods are very sensitive to system identification errors. A
study of the multichannel least-squares method with respect to two classes of characteristic channel zeros is conducted. Accordingly, a relaxed multichannel least-
squares method is proposed. Channel shortening in connection with the multiple-
input/output inverse theorem and the relaxed multichannel least-squares method is
discussed.
Two algorithms taking into account the system identification errors are developed. Firstly, an optimally-stopped weighted conjugate gradient algorithm is
proposed. A conjugate gradient iterative method is employed to compute the equalization system. The iteration process is stopped optimally with respect to system identification errors. Secondly, a system-identification-error-robust equalization
method exploring the use of error models is presented, which incorporates system
identification error models in the weighted multichannel least-squares formulation
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