Particle Filtering

Djurić, Petar M.; Bugallo, Mónica F.

doi:10.1002/9780470575758.ch5

Cited by 102 publications

(153 citation statements)

References 60 publications

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“…Particle filtering, also known as the sequential Monte Carlo method, is a successful technique to recursively estimate hidden states of nonlinear non-Gaussian dynamical systems [34]. The mathematical framework of the particle filtering is not detailed here, but for a good introduction, the interested reader is referred to appropriate papers such as [35] and [36]. To briefly summarize, one particle is composed of a state value, i.e., a hypothesis, and an associated weight, i.e., the probability that this hypothesis is true regarding the observation.…”

Section: Optimal Intersensor Distancementioning

confidence: 99%

Observation of Vehicle Axles Through Pass-by Noise: A Strategy of Microphone Array Design

Marmaroli

Carmona

Odobez

et al. 2013

IEEE Trans. Intell. Transport. Syst.

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Abstract-This paper focuses on road traffic monitoring using sounds and proposes, more specifically, a microphone array design methodology for observing vehicle trajectory from acoustic-based correlation functions. In a former work, authors have shown that combining generalized cross correlation (GCC) functions and a particle filter onto the audio signals simultaneously acquired by two sensors placed near the road allows the joint estimation of the speed and the wheelbase length of road vehicles as they pass by. This is mainly due to the broadband nature of the tire/road noise, which makes their spatial dissociation possible by means of an appropriate GCC processor. At the time, nothing has been said about the best distance to chose between the sensors. A methodology is proposed here to find this optimum, which is expected to improve the observation quality and, thus, the tracking performance. Theoretical developments of this paper are partially assessed with preliminary experiments.

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Section: Optimal Intersensor Distancementioning

confidence: 99%

Observation of Vehicle Axles Through Pass-by Noise: A Strategy of Microphone Array Design

Marmaroli

Carmona

Odobez

et al. 2013

IEEE Trans. Intell. Transport. Syst.

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“…Its goal is to estimate the posterior marginal probability density function (PDF) of the target's position, given the priors, and measurements. Since this approach is generally intractable, it is necessary to use some message-passing method [18] and also to approximate all PDFs using particle-based approximations [19,20]. One suitable framework is nonparametric belief propagation (NBP), which was initially proposed for static networks [9].…”

Section: Related Workmentioning

confidence: 99%

Cooperative localization in mobile networks using nonparametric variants of belief propagation

Savic¹,

Zazo²

2013

Ad Hoc Networks

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Of the many state-of-the-art methods for cooperative localization in wireless sensor networks (WSN), only very few adapt well to mobile networks. The main problems of the well-known algorithms, based on nonparametric belief propagation (NBP), are the high communication cost and inefficient sampling techniques. Moreover, they either do not use smoothing or just apply it offline. Therefore, in this article, we propose more flexible and efficient variants of NBP for cooperative localization in mobile networks. In particular, we provide: i) an optional 1-lag smoothing done almost in real-time, ii) a novel low-cost communication protocol based on package approximation and censoring, iii) higher robustness of the standard mixture importance sampling (MIS) technique, and iv) a higher amount of information in the importance densities by using the population Monte Carlo (PMC) approach, or an auxiliary variable. Through extensive simulations, we confirmed that all the proposed techniques outperform the standard NBP method.

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“…Monte Carlo (MC) algorithms are very popular numerical techniques for the approximation of optimal a posteriori estimators [9,18,13,30]. Particle filters (PFs) are well-known MC methods that have been extensively applied in different fields, in order to handle analytically intractable posterior probability density functions (pdfs) [4,10,15,20].…”

Section: Introductionmentioning

confidence: 99%

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

Martino

Read

Elvira

et al. 2017

Digital Signal Processing

136

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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Particle Filtering

Cited by 102 publications

References 60 publications

Observation of Vehicle Axles Through Pass-by Noise: A Strategy of Microphone Array Design

Observation of Vehicle Axles Through Pass-by Noise: A Strategy of Microphone Array Design

Cooperative localization in mobile networks using nonparametric variants of belief propagation

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

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