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Decentralized Sensor Fusion With Distributed Particle Filters [article]

Matthew Rosencrantz, Geoffrey Gordon, Sebastian Thrun
2012 arXiv   pre-print
This paper presents a scalable Bayesian technique for decentralized state estimation from multiple platforms in dynamic environments.  ...  Our approach is evaluated in the context of a distributed surveillance scenario that arises in a robotic system for playing the game of laser tag.  ...  This observation forms the core of a literature on decentralized sensor fusion, of which (Nettle ton et al., 2000) is an example.  ... 
arXiv:1212.2493v1 fatcat:25wdbzrqs5ai7brxpdhyiamjr4

Multi-robot Cooperative Object Localization [chapter]

João Santos, Pedro Lima
2010 Lecture Notes in Computer Science  
We introduce a multi-robot/sensor cooperative object detection and tracking method based on a decentralized Bayesian approach which uses particle filters to avoid simplifying assumptions about the object  ...  ' particles, and mixes the particles representing its own belief about the object location with particles sampling the received GMM.  ...  [15] introduced a scalable Bayesian technique for decentralized state estimation with distributed particle filters using a selective communication procedure over the particle set.  ... 
doi:10.1007/978-3-642-11876-0_29 fatcat:3kldftt4fffmxcrb4wapwvjp6q

Decentralized and Distributed Information Filter for Autonomous Intelligent Multisensor Systems

A Olatunbosun, I Lawal
2017 Journal of Scientific Research and Reports  
This research paper is on development of distributed and decentralized multisensor data estimation and fusion algorithm with linear information filter for fusing the information from these various sensors  ...  The estimation technique is modified Kalman filter that provides estimates of the information about a certain state.  ...  Decentralized sensor fusion with distributed particle filters. (100 6. 200). t ≤ < (100 200) t ≤ < ( 200) t ≥ Benjamin Noack, Daniel Lyons, Matthias Nagel, Uwe HD.  ... 
doi:10.9734/jsrr/2017/27886 fatcat:w4qebu3rkfbslefpb5ji5mnm6y

Data Fusion Performance Evaluation for Dissimilar Sensors: Application to Road Obstacle Tracking [chapter]

Blanc Christophe, Checchin Paul, Gidel Samuel, Trassoudaine Laurent
2009 Sensor and Data Fusion  
For clarity, results for particle filter are present only with 2000 particles.  ...  This improved accuracy of the particle filter, however, is at the expense of the computational load. Particle filter with 10000 particles is equivalent to EKF.  ...  We propose here, to carry out a particle filtering on the fusion module level. A fusion state is initialized. From this vector, a set of particles is built.  ... 
doi:10.5772/6570 fatcat:xrbwg5d5tbfzdipcf46uf7t5bm

Asynchronous distributed particle filter via decentralized evaluation of Gaussian products

B N Oreshkin, M J Coates
2010 2010 13th International Conference on Information Fusion  
We present a distributed particle filtering algorithm for target tracking in sensor networks. Several existing algorithms rely on the establishment and maintenance of a spanning path or tree.  ...  In our algorithm, nodes run local particle filters and then approximate their local posteriors using Gaussian approximations.  ...  Proposed Distributed Particle Filter In this section we discuss a concrete particle filter implementation using the fusion framework discussed in Section 3.  ... 
doi:10.1109/icif.2010.5712070 fatcat:qzdvopmfkrfqrnktb5upkai55a

Accurate fusion of robot, camera and wireless sensors for surveillance applications

Andrew Gilbert, John Illingworth, Richard Bowden
2009 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops  
A decentralized data fusion algorithm for combining all these information is presented.  ...  We propose an approach that uses three simultaneous separate sensors.  ...  The main issues and problems with decentralized information fusion can be traced back to the work [?]  ... 
doi:10.1109/iccvw.2009.5457462 dblp:conf/iccvw/GilbertIBCM09 fatcat:a5ggglnaozaoncn6a5pkxqaydu

Bayesian Sensor Fusion Methods for Dynamic Object Tracking—A Comparative Study

Ivan Marković, Ivan Petrović
2014 Automatika  
We compare the prospects of utilizing both approaches, and present explicit solutions in the forms of extended information filter, unscented information filter, and particle filter.  ...  We analyze two groups of data fusion methods: centralized independent likelihood fusion, where the sensors report only its measurement to the fusion center, and hierarchical fusion, where each sensor runs  ...  Hierarchical sensor fusion with particle filtering In this hierarchical solution with particle filters each sensor modality runs its own local independent particle filter, which needs to be fused with  ... 
doi:10.7305/automatika.2014.09.847 fatcat:55mm2z3vgrbrzcc7guadu2ezqq

Data fusion in ubiquitous networked robot systems for urban services

Luis Merino, Andrew Gilbert, Jesús Capitán, Richard Bowden, John Illingworth, Aníbal Ollero
2012 Annales des télécommunications  
The estimate from all these sources are then combined using a decentralized data fusion algorithm to provide an increase in performance.  ...  The paper describes the algorithms for tracking with a set of fixed surveillance cameras and the algorithms for position tracking using the signal strength received by a wireless sensor network (WSN).  ...  Acknowledgements This work is partially supported by URUS, Ubiquitous networking Robotics in Urban Settings, funded by the European Commission (EC) under FP6 with contract number FP6-EU-IST-045062.  ... 
doi:10.1007/s12243-012-0311-1 fatcat:zujajiocszc35iby44zapfaixi

A Review of Data Fusion Techniques

Afnan Alofi, Anwaar Alghamdi, Razan Alahmadi, Najla Aljuaid, Hemalatha M.
2017 International Journal of Computer Applications  
General Terms Multi-sensor fusion, data fusion, Kalman filter, Particle filter, Bayesian methods, Dempster-Shafer.  ...  We explain possible data fusion classifications and review the most common fusion methods such as Kalman filter and The Bayesian Methods.  ...  Particle filters generally comprise the following steps: Prediction For each particle, the state of the system is predicted at time k with distribution noise (q), as in equation [6] .  ... 
doi:10.5120/ijca2017914318 fatcat:rhnuzqriw5dzpiqbrtrf2clkkm

2020 Index IEEE Transactions on Signal and Information Processing over Networks Vol. 6

2020 IEEE Transactions on Signal and Information Processing over Networks  
Gaussian Mixture Particle Jump-Markov-CPHD Fusion for Multitarget Tracking Using Sensors With Limited Views.  ...  ., +, TSIPN 2020 426-441 Gaussian Mixture Particle Jump-Markov-CPHD Fusion for Multitarget Tracking Using Sensors With Limited Views.  ... 
doi:10.1109/tsipn.2021.3050691 fatcat:fygxby3zrjbhjlid5odg4mdnf4

2019 Index IEEE Transactions on Signal and Information Processing over Networks Vol. 5

2019 IEEE Transactions on Signal and Information Processing over Networks  
., +, TSIPN June 2019 375-389 Sensor fusion Data Fusion in the Air With Non-Identical Wireless Sensors.  ...  ., +, TSIPN March 2019 168-180 Fusion Rules for Distributed Detection in Clustered Wireless Sensor Networks With Imperfect Channels.  ... 
doi:10.1109/tsipn.2019.2959414 fatcat:ixpx5rg5l5hshkt2ppvie3afqe

A probabilistic framework for entire WSN localization using a mobile robot

F. Caballero, L. Merino, P. Gil, I. Maza, A. Ollero
2008 Robotics and Autonomous Systems  
In the second stage, the nodes refine their position estimates employing a decentralized information filter.  ...  This paper presents a new method for the localization of a Wireless Sensor Network (WSN) by means of collaboration with a robot within a Network Robot System (NRS).  ...  Acknowledgment The authors thank the help of Antidio Viguria for supporting part of the WSN experiments needed to validate the Particle Filter approach presented in this paper.  ... 
doi:10.1016/j.robot.2008.06.003 fatcat:l5wwhevctfgeloka7e4jdywfq4

Using Ensemble Kalman Filter for Distributed Sensor Fusion

Pornsarayouth Sirichai, Masaki Yamakita
2013 Transactions of the Institute of Systems Control and Information Engineers  
On the other hand, using distributed or decentralized sensor fusion with conventional consensus algorithms which do not consider cross-covariance terms among nodes is not considerably efficient.  ...  We propose Ensemble Kalman Filter (EnKF) storing an estimation as a group of distinct particles to determine correlation between estimations.  ...  All fusion algorithms were applied with EnKF. We simulated distributed filters on a random network of 50 nodes. Each node contains one bearing sensor embedded with a distributed filter.  ... 
doi:10.5687/iscie.26.466 fatcat:hoetmplztfepnk3upwebkew26a

Target Tracking in Wireless Sensor Networks [chapter]

Jianxun Li, Yan Zhou
2010 Wireless Sensor Networks: Application-Centric Design  
algorithm based on particle filters.  ...  (UKF), and particle filtering (PF), which involve state estimation using a set of local filters that communicate with all other nodes (see e.g.  ... 
doi:10.5772/13701 fatcat:vohttds5grd5hnasde4bge6dxq

A multiple UAV system for vision-based search and localization

John Tisdale, Allison Ryan, Zu Kim, David Tornqvist, J. Karl Hedrick
2008 2008 American Control Conference  
While a grid-based system for Bayesian estimation was used for the flight demonstrations, the use of a particle filter has also been examined.  ...  Distributed data fusion provides a framework for multiple sensors to search for a target and accurately estimate its position. Vision based sensing is employed, using xed downward-looking cameras.  ...  The case with particles result in the Particle Filter, see [11] . In this paper, where a search task is treated, the distributions are far from Gaussian.  ... 
doi:10.1109/acc.2008.4586784 dblp:conf/amcc/TisdaleRKTH08 fatcat:qhv4zgwwcffxzhf3tywlb4zlfm
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