2 research outputs found
Fighting Sample Degeneracy and Impoverishment in Particle Filters: A Review of Intelligent Approaches
During the last two decades there has been a growing interest in Particle
Filtering (PF). However, PF suffers from two long-standing problems that are
referred to as sample degeneracy and impoverishment. We are investigating
methods that are particularly efficient at Particle Distribution Optimization
(PDO) to fight sample degeneracy and impoverishment, with an emphasis on
intelligence choices. These methods benefit from such methods as Markov Chain
Monte Carlo methods, Mean-shift algorithms, artificial intelligence algorithms
(e.g., Particle Swarm Optimization, Genetic Algorithm and Ant Colony
Optimization), machine learning approaches (e.g., clustering, splitting and
merging) and their hybrids, forming a coherent standpoint to enhance the
particle filter. The working mechanism, interrelationship, pros and cons of
these approaches are provided. In addition, Approaches that are effective for
dealing with high-dimensionality are reviewed. While improving the filter
performance in terms of accuracy, robustness and convergence, it is noted that
advanced techniques employed in PF often causes additional computational
requirement that will in turn sacrifice improvement obtained in real life
filtering. This fact, hidden in pure simulations, deserves the attention of the
users and designers of new filters.Comment: Expert Systems with Applications, 201
Variational Bayes Inference in Digital Receivers
The digital telecommunications receiver is an important context for inference
methodology, the key objective being to minimize the expected loss function in
recovering the transmitted information. For that criterion, the optimal
decision is the Bayesian minimum-risk estimator. However, the computational
load of the Bayesian estimator is often prohibitive and, hence, efficient
computational schemes are required. The design of novel schemes, striking new
balances between accuracy and computational load, is the primary concern of
this thesis. Two popular techniques, one exact and one approximate, will be
studied.
The exact scheme is a recursive one, namely the generalized distributive law
(GDL), whose purpose is to distribute all operators across the conditionally
independent (CI) factors of the joint model, so as to reduce the total number
of operators required. In a novel theorem derived in this thesis, GDL, if
applicable, will be shown to guarantee such a reduction in all cases. An
associated lemma also quantifies this reduction. For practical use, two novel
algorithms, namely the no-longer-needed (NLN) algorithm and the generalized
form of the Markovian Forward-Backward (FB) algorithm, recursively factorizes
and computes the CI factors of an arbitrary model, respectively.
The approximate scheme is an iterative one, namely the Variational Bayes (VB)
approximation, whose purpose is to find the independent (i.e. zero-order
Markov) model closest to the true joint model in the minimum Kullback-Leibler
divergence (KLD) sense. Despite being computationally efficient, this naive
mean field approximation confers only modest performance for highly correlated
models. A novel approximation, namely Transformed Variational Bayes (TVB), will
be designed in the thesis in order to relax the zero-order constraint in the VB
approximation, further reducing the KLD of the optimal approximation.Comment: PhD thesis, Trinity College Dublin, Ireland (2014