126 research outputs found
Advanced optimization algorithms for sensor arrays and multi-antenna communications
Optimization problems arise frequently in sensor array and multi-channel signal processing applications. Often, optimization needs to be performed subject to a matrix constraint. In particular, unitary matrices play a crucial role in communications and sensor array signal processing. They are involved in almost all modern multi-antenna transceiver techniques, as well as sensor array applications in biomedicine, machine learning and vision, astronomy and radars.
In this thesis, algorithms for optimization under unitary matrix constraint stemming from Riemannian geometry are developed. Steepest descent (SD) and conjugate gradient (CG) algorithms operating on the Lie group of unitary matrices are derived. They have the ability to find the optimal solution in a numerically efficient manner and satisfy the constraint accurately. Novel line search methods specially tailored for this type of optimization are also introduced. The proposed approaches exploit the geometrical properties of the constraint space in order to reduce the computational complexity.
Array and multi-channel signal processing techniques are key technologies in wireless communication systems. High capacity and link reliability may be achieved by using multiple transmit and receive antennas. Combining multi-antenna techniques with multicarrier transmission leads to high the spectral efficiency and helps to cope with severe multipath propagation.
The problem of channel equalization in MIMO-OFDM systems is also addressed in this thesis. A blind algorithm that optimizes of a combined criterion in order to be cancel both inter-symbol and co-channel interference is proposed. The algorithm local converge properties are established as well
Linear Predictive Spectral Analysis via the Lp Norm
This study involves linear predictive spectral analysis under the general LP norm; both one dimensional and two dimensional spectral estimation algorithms are developed. The objective in this study is determination of frequency resolution capability for various LP normed solutions to linear predictive spectral estimation equations. A modified residual steepest descent algorithm is utilized to generate the required solution. The research presented in this thesis could not have been accomplished without the support of the Oklahoma State University Research Consortium For Well Log Data Enhancement Via Signal Processing. The member companies of this consortium include Amococ Production Company, Area Oil and Gas Company, Cities Service Oil and Gas Corporation, Conoco, Exxon, IBM, Mobil Research and Development, Phillips Petroleum Corporation, Sohio Petroleum Company, and Texaco.Electrical Engineerin
Blind image deconvolution: nonstationary Bayesian approaches to restoring blurred photos
High quality digital images have become pervasive in modern scientific and everyday life —
in areas from photography to astronomy, CCTV, microscopy, and medical imaging. However
there are always limits to the quality of these images due to uncertainty and imprecision in the
measurement systems. Modern signal processing methods offer the promise of overcoming
some of these problems by postprocessing
these blurred and noisy images. In this thesis,
novel methods using nonstationary statistical models are developed for the removal of blurs
from out of focus and other types of degraded photographic images.
The work tackles the fundamental problem blind image deconvolution (BID); its goal is
to restore a sharp image from a blurred observation when the blur itself is completely unknown.
This is a “doubly illposed”
problem — extreme lack of information must be countered
by strong prior constraints about sensible types of solution. In this work, the hierarchical
Bayesian methodology is used as a robust and versatile framework to impart the required prior
knowledge.
The thesis is arranged in two parts. In the first part, the BID problem is reviewed, along
with techniques and models for its solution. Observation models are developed, with an
emphasis on photographic restoration, concluding with a discussion of how these are reduced
to the common linear spatially-invariant
(LSI) convolutional model. Classical methods for the
solution of illposed
problems are summarised to provide a foundation for the main theoretical
ideas that will be used under the Bayesian framework. This is followed by an indepth
review
and discussion of the various prior image and blur models appearing in the literature, and then
their applications to solving the problem with both Bayesian and nonBayesian
techniques.
The second part covers novel restoration methods, making use of the theory presented in Part I.
Firstly, two new nonstationary image models are presented. The first models local variance in
the image, and the second extends this with locally adaptive noncausal
autoregressive (AR)
texture estimation and local mean components. These models allow for recovery of image
details including edges and texture, whilst preserving smooth regions. Most existing methods
do not model the boundary conditions correctly for deblurring of natural photographs, and a
Chapter is devoted to exploring Bayesian solutions to this topic.
Due to the complexity of the models used and the problem itself, there are many challenges
which must be overcome for tractable inference. Using the new models, three different inference
strategies are investigated: firstly using the Bayesian maximum marginalised a posteriori
(MMAP) method with deterministic optimisation; proceeding with the stochastic methods
of variational Bayesian (VB) distribution approximation, and simulation of the posterior distribution
using the Gibbs sampler. Of these, we find the Gibbs sampler to be the most effective
way to deal with a variety of different types of unknown blurs. Along the way, details are given
of the numerical strategies developed to give accurate results and to accelerate performance.
Finally, the thesis demonstrates state of the art
results in blind restoration of synthetic and real
degraded images, such as recovering details in out of focus photographs
Analog, hybrid, and digital simulation
Analog, hybrid, and digital computerized simulation technique
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