Lattice Particle Filters

By Dirk Ormoneit, Christiane Lemieux, David J. Fleet. 2001

26 citations recorded by the source corpus.

A promising approach to approximate inference in state-space models is particle filtering. However, the performance of particle filters often varies significantly due to their stochastic nature. We present a class of algorithms, called lattice particle filters, that circumvent this difficulty by placing the particles deterministically according to a Quasi-Monte Carlo integration rule. We describe a practical realization of this idea, discuss its theoretical properties, and its efficiency. Ex~ perimental results with a synthetic 2D tracking problem show that the lattice particle filter is equivalent to a conventional particle filter that has between 10 and 60% more particles, depending on their "sparsity" in the state-space. We also present results on inferring 3D human motion from moving light displays.

Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods

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Citations in the corpus

Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering (2015, methodology): “By replacing random sampling with a quasi-random number sequence, we obtain the already proposed sequential quasi-Monte Carlo scheme (Philomin et al., 2000; Ormoneit et al., 2001; Gerber and Chopin, 2014).”
Monte Carlo and Quasi-Monte Carlo Sampling (2009, methodology): “In addition to the reference [354] mentioned above, other papers that discuss the use of quasi–Monte Carlo within bootstrap filters are [117, 376].”