PPF: A parallel particle filtering library

Demirel, Ömer Burak; Smal, Ihor; Niessen, Wiro J.; Meijering, Erik; Sbalzarini, Ivo F.

doi:10.1049/cp.2014.0529

Cited by 10 publications

(10 citation statements)

References 48 publications

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“…The particles are initialized at the ground-truth location and all tests are repeated 50 times for different realizations of the image-noise process on a single core of a 12-core Intel R Xeon R E5-2640 2.5 GHz CPU with 128 GB DDR3 800 MHz memory on MPI-CBG's MadMax computer cluster. All algorithms are implemented in Java (v. 1.7.0 13) within the Parallel Particle Filtering (PPF) library [28]. The results are summarized in Figs.…”

Section: Methodsmentioning

confidence: 99%

Piecewise Constant Sequential Importance Sampling for Fast Particle Filtering

Demirel

Smal

Niessen

et al. 2014

IET Conference on Data Fusion &Amp; Target Tracking 2014: Algorithms and Applications

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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 likelihood with a mixture of uniform distributions over pre-defined cells or bins. The particles in each bin are represented by a dummy particle at the center of mass of the original particle distribution and with a state vector that is the average of the states of all particles in the same bin. The likelihood is only evaluated for the dummy particles, and the resulting weight is identically assigned to all particles in the bin. We derive upper bounds on the approximation error of the so-obtained piecewise constant function representation, and analyze how bin size affects tracking accuracy and runtime. Further, we show numerically that the pcSIR approximation error converges to that of sequential importance sampling/resampling (SIR) as the bin size is decreased. We present a set of numerical experiments from the field of biological image processing and tracking that demonstrate pcSIR's capabilities. Overall, we consider pcSIR a promising candidate for simple, fast particle filtering in generic applications, especially in those with a costly likelihood update step.

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Section: Methodsmentioning

confidence: 99%

Piecewise Constant Sequential Importance Sampling for Fast Particle Filtering

Demirel

Smal

Niessen

et al. 2014

IET Conference on Data Fusion &Amp; Target Tracking 2014: Algorithms and Applications

Self Cite

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“…Indeed, each IS scheme can compute independentlyw (m) k,t and Z k,t , and then they merge all the information for calculatingρ k,t (see Figure 1). Therefore, we consider K parallel particle filters (as in [8,12,13], for instance) using the transition model as proposal pdf 7 φ k,t (x k,t |x k,1:t−1 ) = q k,t (x k,t |x k,t−1 ), each one tracking a different states-space model M k , for k = 1, . .…”

Section: Model Averaging Particle Filtersmentioning

confidence: 99%

“…. = M K = T , then MAPF described a distributed PF scheme [8,12,15] which K parallel PFs cooperate for providing a global estimator, I 1:t = K k=1ρ k,t I k,1:t . The computational effort is distributed in order to foster the filters that are providing the best performance, in the specific run.…”

Section: Sub-cases Of Interestmentioning

confidence: 99%

“…The first two numerical examples employ artificial data whereas the last one involves real data. 12 More specifically, in the first example we study the ability of MAPF in a time-varying model scenario. The second example considers the parameter selection and tracking problem in a state-space model (the parameter selection can be interpreted as a sub-case of the model selection problem).…”

Section: Numerical Simulationsmentioning

confidence: 99%

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Cooperative parallel particle filters for online model selection and applications to urban mobility

Martino

Read

Elvira

et al. 2017

Digital Signal Processing

132

112

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We design a sequential Monte Carlo scheme for the dual purpose of Bayesian inference and model selection. We consider the application context of urban mobility, where several modalities of transport and different measurement devices can be employed. Therefore, we address the joint problem of online tracking and detection of the current modality. For this purpose, we use interacting parallel particle filters, each one addressing a different model. They cooperate for providing a global estimator of the variable of interest and, at the same time, an approximation of the posterior density of each model given the data. The interaction occurs by a parsimonious distribution of the computational effort, with online adaptation for the number of particles of each filter according to the posterior probability of the corresponding model. The resulting scheme is simple and flexible. We have tested the novel technique in different numerical experiments with artificial and real data, which confirm the robustness of the proposed scheme.

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“…Balasingam et al [6] suggest an optimal particle selection for RNAs. Demirel et al [7] provide a parallel particle filtering library containing a couple of particle exchange algorithms. Recently, distributed particle filters with a proof of convergence have been introduced.…”

Section: Introductionmentioning

confidence: 99%

Distributed particle filtering under real-time constraints

Bozkurt

Cemgil

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Particle filters are powerful methods for state estimation in nonlinear/non-Gaussian dynamical systems. However due to the heavy computational requirements, they may not satisfy the real-time constraints in many applications requiring a large number of particles. By means of distributed implementation, real-time particle filtering can be achieved. However, the resampling stage in particle filters requires particle interaction which causes communication overhead. In this work, we propose a distributed resampling algorithm based on Butterfly Resampling previously described in the literature. We describe three interaction schemes (i) the complete interaction, (ii) the pairwise interaction where the nodes are constrained to communicate in pairs and (iii) the partial pairwise interaction in which only one pair is allowed to communicate. The goal is to diminish the communication cost in exchange for negligible loss of effective sample size. We conduct experiments on a cluster environment and compare our methods in terms of execution time, communication time and effective sample size. We find that the sparse interaction schemes show better performance for distributed systems and they keep the effective sample size nearly as high as the complete interaction scheme does.

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PPF: A parallel particle filtering library

Cited by 10 publications

References 48 publications

Piecewise Constant Sequential Importance Sampling for Fast Particle Filtering

Piecewise Constant Sequential Importance Sampling for Fast Particle Filtering

Cooperative parallel particle filters for online model selection and applications to urban mobility

Distributed particle filtering under real-time constraints

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