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

Martino, Luca; Read, Jesse; Elvira, Vı́ctor; Louzada, Francisco

doi:10.1016/j.dsp.2016.09.011

Cited by 131 publications

(110 citation statements)

References 36 publications

(89 reference statements)

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“…Particle filters (PFs) are implementations of recursive Bayesian filters which approximate the posterior PDF by a set of random samples, called particles, with associated weights. Several types of PFs have been developed over the last few years [1][2][3][4][5][6][7][8]. They differ in their choice of the importance sampling density and the resampling step.…”

Section: Introductionmentioning

confidence: 99%

Log-PF: Particle Filtering in Logarithm Domain

Gentner

Zhang

Jost

2018

Journal of Electrical and Computer Engineering

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This paper presents a particle filter, called Log-PF, based on particle weights represented on a logarithmic scale. In practical systems, particle weights may approach numbers close to zero which can cause numerical problems. Therefore, calculations using particle weights and probability densities in the logarithmic domain provide more accurate results. Additionally, calculations in logarithmic domain improve the computational efficiency for distributions containing exponentials or products of functions. To provide efficient calculations, the Log-PF exploits the Jacobian logarithm that is used to compute sums of exponentials. We introduce the weight calculation, weight normalization, resampling, and point estimations in logarithmic domain. For point estimations, we derive the calculation of the minimum mean square error (MMSE) and maximum a posteriori (MAP) estimate. In particular, in situations where sensors are very accurate the Log-PF achieves a substantial performance gain. We show the performance of the derived Log-PF by three simulations, where the Log-PF is more robust than its standard particle filter counterpart. Particularly, we show the benefits of computing all steps in logarithmic domain by an example based on Rao-Blackwellization.

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Section: Introductionmentioning

confidence: 99%

Log-PF: Particle Filtering in Logarithm Domain

Gentner

Zhang

Jost

2018

Journal of Electrical and Computer Engineering

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“…On the contrary, an SMC-based alternative for filtering time-series of different properties could be to pose the problem from a model selection perspective [36][37][38]. However, we study a different and more ambitious approach in this paper, as we target inference of latent states with different characteristics in a unified and consistent manner.…”

Section: Contributionmentioning

confidence: 99%

Sequential Monte Carlo for inference of latent ARMA time-series with innovations correlated in time

Urteaga

Bugallo

Djurić

2017

EURASIP J. Adv. Signal Process.

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We consider the problem of sequential inference of latent time-series with innovations correlated in time and observed via nonlinear functions. We accommodate time-varying phenomena with diverse properties by means of a flexible mathematical representation of the data. We characterize statistically such time-series by a Bayesian analysis of their densities. The density that describes the transition of the state from time t to the next time instant t + 1 is used for implementation of novel sequential Monte Carlo (SMC) methods. We present a set of SMC methods for inference of latent ARMA time-series with innovations correlated in time for different assumptions in knowledge of parameters. The methods operate in a unified and consistent manner for data with diverse memory properties. We show the validity of the proposed approach by comprehensive simulations of the challenging stochastic volatility model.

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“…Related to the problem under study, it is worth saying that several contributions deal with the filtering problem in nonlinear/non-Gaussian SSM under model uncertainty using sequential Monte Carlo (SMC) methods, for instance, joint state and parameter estimation solutions [20], model selection strategies using interacting parallel PFs [21,22], or model information fusion within the SMC formulation [23]. The main drawback of SMC methods is their high computational complexity and the curse-ofdimensionality [24].…”

Section: State-of-the-artmentioning

confidence: 99%

NLOS mitigation in indoor localization by marginalized Monte Carlo Gaussian smoothing

Vilà‐Valls

Closas

2017

EURASIP J. Adv. Signal Process.

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One of the main challenges in indoor time-of-arrival (TOA)-based wireless localization systems is to mitigate non-line-of-sight (NLOS) propagation conditions, which degrade the overall positioning performance. The positive skewed non-Gaussian nature of TOA observations under LOS/NLOS conditions can be modeled as a heavy-tailed skew t-distributed measurement noise. The main goal of this article is to provide a robust Bayesian inference framework to deal with target localization under NLOS conditions. A key point is to take advantage of the conditionally Gaussian formulation of the skew t-distribution, thus being able to use computationally light Gaussian filtering and smoothing methods as the core of the new approach. The unknown non-Gaussian noise latent variables are marginalized using Monte Carlo sampling. Numerical results are provided to show the performance improvement of the proposed approach.

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

Cited by 131 publications

References 36 publications

Log-PF: Particle Filtering in Logarithm Domain

Log-PF: Particle Filtering in Logarithm Domain

Sequential Monte Carlo for inference of latent ARMA time-series with innovations correlated in time

NLOS mitigation in indoor localization by marginalized Monte Carlo Gaussian smoothing

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