3,933 research outputs found
Direction finding for an extended target with possibly non-symmetric spatial spectrum
We consider the problem of estimating the direction of arrival (DOA) of an extended target in radar array processing. Two algorithms are proposed that do not assume that the power azimuthal distribution of the scatterers is symmetric with respect to the mass center of the target. The first one is based on spectral moments which are easily related to the target’s DOA. The second method stems from a previous paper by the present authors and consists of a least-squares fit on the elements of the covariance matrix. Both methods are simple and are shown to provide accurate estimates. Furthermore, they extend the range of unambiguous
DOAs that can be estimated, compared with the same previous paper
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Imaging spectrometers measure electromagnetic energy scattered in their
instantaneous field view in hundreds or thousands of spectral channels with
higher spectral resolution than multispectral cameras. Imaging spectrometers
are therefore often referred to as hyperspectral cameras (HSCs). Higher
spectral resolution enables material identification via spectroscopic analysis,
which facilitates countless applications that require identifying materials in
scenarios unsuitable for classical spectroscopic analysis. Due to low spatial
resolution of HSCs, microscopic material mixing, and multiple scattering,
spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus,
accurate estimation requires unmixing. Pixels are assumed to be mixtures of a
few materials, called endmembers. Unmixing involves estimating all or some of:
the number of endmembers, their spectral signatures, and their abundances at
each pixel. Unmixing is a challenging, ill-posed inverse problem because of
model inaccuracies, observation noise, environmental conditions, endmember
variability, and data set size. Researchers have devised and investigated many
models searching for robust, stable, tractable, and accurate unmixing
algorithms. This paper presents an overview of unmixing methods from the time
of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models
are first discussed. Signal-subspace, geometrical, statistical, sparsity-based,
and spatial-contextual unmixing algorithms are described. Mathematical problems
and potential solutions are described. Algorithm characteristics are
illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensin
Direct and Inverse Computational Methods for Electromagnetic Scattering in Biological Diagnostics
Scattering theory has had a major roll in twentieth century mathematical
physics. Mathematical modeling and algorithms of direct,- and inverse
electromagnetic scattering formulation due to biological tissues are
investigated. The algorithms are used for a model based illustration technique
within the microwave range. A number of methods is given to solve the inverse
electromagnetic scattering problem in which the nonlinear and ill-posed nature
of the problem are acknowledged.Comment: 61 pages, 5 figure
Time resolved tracking of a sound scatterer in a turbulent flow: non-stationary signal analysis and applications
It is known that ultrasound techniques yield non-intrusive measurements of
hydrodynamic flows. For example, the study of the echoes produced by a large
number of particle insonified by pulsed wavetrains has led to a now standard
velocimetry technique. In this paper, we propose to extend the method to the
continuous tracking of one single particle embedded in a complex flow. This
gives a Lagrangian measurement of the fluid motion, which is of importance in
mixing and turbulence studies. The method relies on the ability to resolve in
time the Doppler shift of the sound scattered by the continuously insonfied
particle.
For this signal processing problem two classes of approaches are used:
time-frequency analysis and parametric high resolution methods. In the first
class we consider the spectrogram and reassigned spectrogram, and we apply it
to detect the motion of a small bead settling in a fluid at rest. In more
non-stationary turbulent flows where methods in the second class are more
robust, we have adapted an Approximated Maximum Likelihood technique coupled
with a generalized Kalman filter.Comment: 16 pages 9 figure
Guaranteed passive parameterized model order reduction of the partial element equivalent circuit (PEEC) method
The decrease of IC feature size and the increase of operating frequencies require 3-D electromagnetic methods, such as the partial element equivalent circuit (PEEC) method, for the analysis and design of high-speed circuits. Very large systems of equations are often produced by 3-D electromagnetic methods. During the circuit synthesis of large-scale digital or analog applications, it is important to predict the response of the system under study as a function of design parameters, such as geometrical and substrate features, in addition to frequency (or time). Parameterized model order reduction (PMOR) methods become necessary to reduce large systems of equations with respect to frequency and other design parameters. We propose an innovative PMOR technique applicable to PEEC analysis, which combines traditional passivity-preserving model order reduction methods and positive interpolation schemes. It is able to provide parametric reduced-order models, stable, and passive by construction over a user-defined range of design parameter values. Numerical examples validate the proposed approach
Matrix probing: a randomized preconditioner for the wave-equation Hessian
This paper considers the problem of approximating the inverse of the
wave-equation Hessian, also called normal operator, in seismology and other
types of wave-based imaging. An expansion scheme for the pseudodifferential
symbol of the inverse Hessian is set up. The coefficients in this expansion are
found via least-squares fitting from a certain number of applications of the
normal operator on adequate randomized trial functions built in curvelet space.
It is found that the number of parameters that can be fitted increases with the
amount of information present in the trial functions, with high probability.
Once an approximate inverse Hessian is available, application to an image of
the model can be done in very low complexity. Numerical experiments show that
randomized operator fitting offers a compelling preconditioner for the
linearized seismic inversion problem.Comment: 21 pages, 6 figure
Generalized linear sampling method for elastic-wave sensing of heterogeneous fractures
A theoretical foundation is developed for active seismic reconstruction of
fractures endowed with spatially-varying interfacial condition
(e.g.~partially-closed fractures, hydraulic fractures). The proposed indicator
functional carries a superior localization property with no significant
sensitivity to the fracture's contact condition, measurement errors, and
illumination frequency. This is accomplished through the paradigm of the
-factorization technique and the recently developed Generalized
Linear Sampling Method (GLSM) applied to elastodynamics. The direct scattering
problem is formulated in the frequency domain where the fracture surface is
illuminated by a set of incident plane waves, while monitoring the induced
scattered field in the form of (elastic) far-field patterns. The analysis of
the well-posedness of the forward problem leads to an admissibility condition
on the fracture's (linearized) contact parameters. This in turn contributes
toward establishing the applicability of the -factorization method,
and consequently aids the formulation of a convex GLSM cost functional whose
minimizer can be computed without iterations. Such minimizer is then used to
construct a robust fracture indicator function, whose performance is
illustrated through a set of numerical experiments. For completeness, the
results of the GLSM reconstruction are compared to those obtained by the
classical linear sampling method (LSM)
- …