Sequential Stratified Sampling Belief Propagation for Multiple Targets Tracking

Xue, Jianru; Zheng, Nanning; Zhong, Xiaopin

doi:10.1007/11538059_35

Cited by 4 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[9] and references therein), as well as more recent attempts to learn models from data [10], [11]. A separate, but related body of work generates consensus among the posterior distributions of distributed filters operating in a specific graph or network topology.…”

Section: A Bayesian Filteringmentioning

confidence: 98%

Bayesian filtering without an observation model

Jones

Mitter

Saligrama

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

For the past half century and more, Bayesian methods such as the Wiener filter, the Kalman filter, and the particle filter have been successfully employed to incorporate observational information into sequential estimates of a timevarying state. However, there are significant difficulties in the application of these methods to distributed, potentially non-engineered systems. First, the methods generally require knowledge of an 'observation model' which specifies the likelihood of any possible observation given any possible state. Specifying such a model, especially in a distributed system, can be extremely difficult. Second, local information dynamics can lead to differences of opinion among distributed agents, particularly when local observation models are autonomously generated and obscured from the rest of the system. In this paper, we use information geometry to develop a decentralized Bayesian inference mechanism for sequential filtering based not on local probability distributions or known observation models, but on local expert state estimates. We demonstrate the efficacy of the proposed mechanism through simulation.

show abstract

Section: A Bayesian Filteringmentioning

confidence: 98%

Bayesian filtering without an observation model

Jones

Mitter

Saligrama

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

show abstract

“…However, Monte Carlo computing in these methods depends heavily on sampling positions. To compensate for the insufficiency caused by the absence of information from belief of nodes, we proposed a stratified sampling belief propagation algorithm to estimate the belief of each node [32,33] , and applied the algorithm successfully in tracking multiple visual targets.…”

Section: Information Processing Technologymentioning

confidence: 99%

“…We propose a PGM-based MTT tracker which integrates information from both the detection process and the interactions among targets to achieve robust tracking [32] . The basic idea of the method is: modeling the joint state of targets and interactions among targets with a coupled Markov Random Field (MRF), and solving the MRF using a sequential stratified sampling belief propagating algorithm [32,33] .…”

Section: Multi-target Trackingmentioning

confidence: 99%

See 1 more Smart Citation

Perceptual stimulus — A Bayesian-based integration of multi-visual-cue approach and its application

et al. 2008

Self Cite

View full text Add to dashboard Cite

“…They arise in many engineer applications, such as robot manipulators [4] , power systems [5] , multiagent models [6−8] , etc. The stability of a switched system can be ensured by a common Lyapunov function (CLF) of all switching modes under arbitrary switching law [9,10] .…”

Section: Introductionmentioning

confidence: 99%

Stability of switched nonlinear systems via extensions of LaSalle’s invariance principle

Wang

Cheng

2009

Sci. China Ser. F-Inf. Sci.

View full text Add to dashboard Cite

This paper studies the extension of LaSalle's invariance principle for switched nonlinear systems. Unlike most existing results in which each switching mode in the system needs to be asymptotically stable, this paper allows the switching modes to be only stable. Under certain ergodicity assumptions of the switching signals, two extensions of LaSalle's invariance principle for global asymptotic stability of switched nonlinear systems are obtained using the method of common joint Lyapunov function.LaSalle's invariance principle, switched systems, weak common Lyapunov function, asymptotically stable

show abstract

Sequential Stratified Sampling Belief Propagation for Multiple Targets Tracking

Cited by 4 publications

References 10 publications

Bayesian filtering without an observation model

Bayesian filtering without an observation model

Perceptual stimulus — A Bayesian-based integration of multi-visual-cue approach and its application

Stability of switched nonlinear systems via extensions of LaSalle’s invariance principle

Contact Info

Product

Resources

About