  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.
