Simultaneous localization and tracking via real-time nonparametric belief propagation

Savic, Vladimir; Wymeersch, Henk

doi:10.1109/icassp.2013.6638650

Cited by 14 publications

(4 citation statements)

References 16 publications

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“…This is no longer the case at n = 20, and indeed the RMSE of p i here converges approximately to a minimum in only q = 1 iteration. Thus, one can obtain low communication cost without compromising the convergence of p i by performing only one message passing iteration per time step (Q = 1, which is sometimes referred to as "real-time BP" [36]). We also see that at n = 10 and n = 20, remarkably, the RMSEs of CoSLAS are similar to or only slightly higher than those of ClkRef and LocRef.…”

Section: B Simulation Resultsmentioning

confidence: 99%

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Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks

Etzlinger

Meyer

Hlawatsch

et al. 2017

IEEE Trans. Signal Process.

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Abstract-Cooperative localization in agent networks based on interagent time-of-flight measurements is closely related to synchronization. To leverage this relation, we propose a Bayesian factor graph framework for cooperative simultaneous localization and synchronization (CoSLAS). This framework is suited to mobile agents and time-varying local clock parameters. Building on the CoSLAS factor graph, we develop a distributed (decentralized) belief propagation algorithm for CoSLAS in the practically important case of an affine clock model and asymmetric time stamping. Our algorithm allows for real-time operation and is suitable for a time-varying network connectivity. To achieve high accuracy at reduced complexity and communication cost, the algorithm combines particle implementations with parametric message representations and takes advantage of a conditional independence property. Simulation results demonstrate the good performance of the proposed algorithm in a challenging scenario with time-varying network connectivity.

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Section: B Simulation Resultsmentioning

confidence: 99%

“…. This is done by calculating the expressions in (35) and (36), respectively, in which q is replaced by Q, the summation index set T…”

Section: Calculation Of Beliefsmentioning

confidence: 99%

Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks

Etzlinger

Meyer

Hlawatsch

et al. 2017

IEEE Trans. Signal Process.

Self Cite

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“…In [19], a source localization problem is solved in the presence of sensor location errors. In [20] and [21], the graph model based algorithms are proposed to solve the simultaneous self-localization and tracking in cooperative localization scenarios. However, all of these papers focus on active localizations.…”

Section: Introductionmentioning

confidence: 99%

TOA-based passive localization of multiple targets with inaccurate receivers based on belief propagation on factor graph

Yuan

Wang

et al. 2016

Digital Signal Processing

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Location awareness is now becoming a vital requirement for many practical applications. In this paper, we consider passive localization of multiple targets with one transmitter and several receivers based on time of arrival (TOA) measurements. Existing studies assume that positions of receivers are perfectly known. However, in practice, receivers' positions might be inaccurate, which leads to localization error of targets. We propose factor graph (FG)-based belief propagation (BP) algorithms to locate the passive targets and improve the position accuracy of receivers simultaneously. Due to the nonlinearity of the likelihood function, messages on the FG cannot be derived in closed form. We propose both sample-based and parametric methods to solve this problem. In the sample-based BP algorithm, particle swarm optimization is employed to reduce the number of particles required to represent messages. In parametric BP algorithm, the nonlinear terms in performs slightly worse than the sample-based BP method, it could be more attractive in practical applications due to the significantly lower computational complexity.

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“…when the whole set of particles of B η j (x j ) is used [10], instead of K η j sets of sigma points. For the calculation of the weights, Eq.…”

Section: The Spawn-spmentioning

confidence: 99%

Efficient cooperative localization algorithm in LOS/NLOS environments

Jin

Yin

Fritsche

et al. 2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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The well-known cooperative localization algorithm, 'sumproduct algorithm over a wireless network' (SPAWN) has two major shortcomings, a relatively high computational complexity and a large communication load. Using the Gaussian mixture model with a model selection criterion and the sigma-point (SP) methods, we propose the SPAWN-SP to overcome these problems. The SPAWN-SP easily accommodates different localization scenarios due to its high flexibility in message representation. Furthermore, harsh LOS/NLOS environments are considered for the evaluation of cooperative localization algorithms. Our simulation results indicate that the proposed SPAWN-SP demonstrates high localization accuracy in different localization scenarios, thanks to its high flexibility in message representation.

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Simultaneous localization and tracking via real-time nonparametric belief propagation

Cited by 14 publications

References 16 publications

Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks

Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks

TOA-based passive localization of multiple targets with inaccurate receivers based on belief propagation on factor graph

Efficient cooperative localization algorithm in LOS/NLOS environments

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