IET Conference on Data Fusion &Amp; Target Tracking 2014: Algorithms and Applications 2014
DOI: 10.1049/cp.2014.0528
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Piecewise Constant Sequential Importance Sampling for Fast Particle Filtering

Abstract: Abstract. Particle filters are key algorithms for object tracking under non-linear, non-Gaussian dynamics. The high computational cost of particle filters, however, hampers their applicability in cases where the likelihood model is costly to evaluate, or where large numbers of particles are required to represent the posterior. We introduce the piecewise constant sequential importance sampling/resampling (pcSIR) algorithm, which aims at reducing the cost of traditional particle filters by approximating the like… Show more

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Cited by 3 publications
(3 citation statements)
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“…While not outperforming in the benchmarks, SVGD is an algorithmically interesting method that offers ample opportunity for optimization and links to established mathematical frameworks such as particle filters [4] for which efficient parallel software exists [5] While our implementation of SVGD was significantly faster than HMC, it was overly sensitive to missing peaks as seen in case of the E05 mixture.…”
Section: Discussionmentioning
confidence: 98%
“…While not outperforming in the benchmarks, SVGD is an algorithmically interesting method that offers ample opportunity for optimization and links to established mathematical frameworks such as particle filters [4] for which efficient parallel software exists [5] While our implementation of SVGD was significantly faster than HMC, it was overly sensitive to missing peaks as seen in case of the E05 mixture.…”
Section: Discussionmentioning
confidence: 98%
“…This algorithm is called Piecewise Constant Sequential Importance Sampling (pcSIR) and can offer significant speedups [34].…”
Section: Piecewise Constant Sequential Importance Resamplingmentioning
confidence: 99%
“…The PPF library also provides an implementation of a fast approximate SIR algorithm that uses a piecewise constant approximation of the likelihood function to estimate the posterior distribution faster. This algorithm is called Piecewise Constant Sequential Importance Sampling (pcSIR) and can offer significant speedups [34].…”
Section: Piecewise Constant Sequential Importance Resamplingmentioning
confidence: 99%