Nonparametric belief propagation for distributed tracking of robot networks with noisy inter-distance measurements

Schiff, Jeremy; Sudderth, Erik B.; Goldberg, Ken

doi:10.1109/iros.2009.5354772

Cited by 16 publications

(17 citation statements)

References 37 publications

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“…Repeating these static localization algorithms can provide location estimates in mobile networks, but this approach is suboptimal due to the lack of additional information provided by the mobility of the sensor nodes. Some works already take this information into account, for example [13,14,15,16,17]. Moreover, the goal of most localization methods [6,8,11,12,13] is just to estimate the position of all the target nodes, without associated uncertainty.…”

Section: Related Workmentioning

confidence: 99%

“…One suitable framework is nonparametric belief propagation (NBP), which was initially proposed for static networks [9]. Variants of this method have been already used for cooperative localization in mobile networks [16,17]. In [16], the authors propose a particle-based distributed message passing method defined on a factor graph.…”

Section: Related Workmentioning

confidence: 99%

“…Comparing with NBP, which is defined on a Markov Random Field (MRF), the main difference is the capability to work with higher-order potentials. In [17], the authors use NBP method for distributed tracking of mobile robots. For this application, it is also necessary to estimate the speed of the targets (not only the positions).…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Cooperative localization in mobile networks using nonparametric variants of belief propagation

Savic¹,

Zazo²

2013

Ad Hoc Networks

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“…There are two well-known frameworks in the state-of-the-art: one based on factor graph [16], and other based on Markov Random Field [17]. The latter one is sufficient for the distance-based method (as in our case).…”

Section: A Belief Propagation (Bp)mentioning

confidence: 99%

Reducing Communication Overhead for Cooperative Localization Using Nonparametric Belief Propagation

Savic

Zazo

2012

IEEE Wireless Commun. Lett.

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Abstract-A number of methods for cooperative localization has been proposed, but most of them provide only location estimate, without associated uncertainty. On the other hand, nonparametric belief propagation (NBP), which provides approximated posterior distributions of the location estimates, is expensive mostly because of the transmission of the particles. In this paper, we propose a novel approach to reduce communication overhead for cooperative positioning using NBP. It is based on: i) communication of the beliefs (instead of the messages), ii) approximation of the belief with Gaussian mixture of very few components, and iii) censoring. According to our simulations results, these modifications reduce significantly communication overhead while providing the estimates almost as accurate as the transmission of the particles.

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“…Non-Gaussian uncertainty is a common occurrence in real-world sensor localization problems, where typically there is a fraction of highly erroneous (outlier) measurements. This problem can be solved using non-parametric probabilistic (or Bayesian) methods [11][12][13][14], which take into account uncertainty of the measurements. They estimate the particle-based approximation of the posterior probability density function (PDF) of the positions of all http://asp.eurasipjournals.com/content/2013/1/16 unknown nodes, given the likelihood and a prior PDF of the positions of all unknown nodes.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric generalized belief propagation based on pseudo-junction tree for cooperative localization in wireless networks

Savic

Zazo

2013

EURASIP J. Adv. Signal Process.

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Non-parametric belief propagation (NBP) is a well-known message passing method for cooperative localization in wireless networks. However, due to the over-counting problem in the networks with loops, NBP's convergence is not guaranteed, and its estimates are typically less accurate. One solution for this problem is non-parametric generalized belief propagation based on junction tree. However, this method is intractable in large-scale networks due to the high-complexity of the junction tree formation, and the high-dimensionality of the particles. Therefore, in this article, we propose the non-parametric generalized belief propagation based on pseudo-junction tree (NGBP-PJT). The main difference comparing with the standard method is the formation of pseudo-junction tree, which represents the approximated junction tree based on thin graph. In addition, in order to decrease the number of high-dimensional particles, we use more informative importance density function, and reduce the dimensionality of the messages. As by-product, we also propose NBP based on thin graph (NBP-TG), a cheaper variant of NBP, which runs on the same graph as NGBP-PJT. According to our simulation and experimental results, NGBP-PJT method outperforms NBP and NBP-TG in terms of accuracy, computational, and communication cost in reasonably sized networks.

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Nonparametric belief propagation for distributed tracking of robot networks with noisy inter-distance measurements

Cited by 16 publications

References 37 publications

Cooperative localization in mobile networks using nonparametric variants of belief propagation

Cooperative localization in mobile networks using nonparametric variants of belief propagation

Reducing Communication Overhead for Cooperative Localization Using Nonparametric Belief Propagation

Nonparametric generalized belief propagation based on pseudo-junction tree for cooperative localization in wireless networks

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