Consensus based distributed particle filter in sensor networks
This paper presents a distributed particle filter over sensor networks. We propose two major steps to make a particle filter to work in a distributed way. The first step is the estimation of global mean and covariance of weighted particles by using an average consensus filter. The second step is the...
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| creator | Dongbing Gu Junxi Sun Zhen Hu Hongzuo Li |
| description | This paper presents a distributed particle filter over sensor networks. We propose two major steps to make a particle filter to work in a distributed way. The first step is the estimation of global mean and covariance of weighted particles by using an average consensus filter. The second step is the propagation of the estimated global mean and covariance through state transition distribution and likelihood distribution by using an unscented transformation. Through this transformation, partial high order information of the estimated global mean and covariance can be incorporated into the estimates for non-linear models. Simulations of tracking tasks in a sensor network with 100 sensor nodes are given. |
| doi_str_mv | 10.1109/ICINFA.2008.4608015 |
| format | Conference Proceeding |
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Simulations of tracking tasks in a sensor network with 100 sensor nodes are given.</description><subject>Equations</subject><subject>Information filters</subject><subject>Mathematical model</subject><subject>Message passing</subject><subject>Particle filters</subject><subject>Probability density function</subject><subject>Robot sensing systems</subject><isbn>1424421837</isbn><isbn>9781424421831</isbn><isbn>1424421845</isbn><isbn>9781424421848</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFj1FLwzAUhSMy0M39gr3kD7Te2yRN-yKM4rQw9EWfR9rcQLS2I8kQ_70dDjwv5x7Ox4XD2AYhR4T6vm3al902LwCqXJZQAaortkRZSFlgJdX1fxB6wZZnsAZEjTdsHeMHzJJKCF3dsodmGiON8RR5ZyJZbn1MwXenNN9HE5LvB-LOD4kC9yM_s1PgI6XvKXzGO7ZwZoi0vviKve8e35rnbP_61DbbfeZRq5T1whTWCVRlRSSVsdRbW1cCdOdqSwKsFaWroUcg6qw1ONfauJmUBrQTK7b5--uJ6HAM_suEn8NlvPgFCUxOAA</recordid><startdate>200806</startdate><enddate>200806</enddate><creator>Dongbing Gu</creator><creator>Junxi Sun</creator><creator>Zhen Hu</creator><creator>Hongzuo Li</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200806</creationdate><title>Consensus based distributed particle filter in sensor networks</title><author>Dongbing Gu ; Junxi Sun ; Zhen Hu ; Hongzuo Li</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-c3a2df31568ee45adecdd98307bf9de30dd36f90c10eebdda1dd97af5ad4a07f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Equations</topic><topic>Information filters</topic><topic>Mathematical model</topic><topic>Message passing</topic><topic>Particle filters</topic><topic>Probability density function</topic><topic>Robot sensing systems</topic><toplevel>online_resources</toplevel><creatorcontrib>Dongbing Gu</creatorcontrib><creatorcontrib>Junxi Sun</creatorcontrib><creatorcontrib>Zhen Hu</creatorcontrib><creatorcontrib>Hongzuo Li</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Dongbing Gu</au><au>Junxi Sun</au><au>Zhen Hu</au><au>Hongzuo Li</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Consensus based distributed particle filter in sensor networks</atitle><btitle>2008 International Conference on Information and Automation</btitle><stitle>ICINFA</stitle><date>2008-06</date><risdate>2008</risdate><spage>302</spage><epage>307</epage><pages>302-307</pages><isbn>1424421837</isbn><isbn>9781424421831</isbn><eisbn>1424421845</eisbn><eisbn>9781424421848</eisbn><abstract>This paper presents a distributed particle filter over sensor networks. We propose two major steps to make a particle filter to work in a distributed way. The first step is the estimation of global mean and covariance of weighted particles by using an average consensus filter. The second step is the propagation of the estimated global mean and covariance through state transition distribution and likelihood distribution by using an unscented transformation. Through this transformation, partial high order information of the estimated global mean and covariance can be incorporated into the estimates for non-linear models. Simulations of tracking tasks in a sensor network with 100 sensor nodes are given.</abstract><pub>IEEE</pub><doi>10.1109/ICINFA.2008.4608015</doi><tpages>6</tpages></addata></record> |
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| ispartof | 2008 International Conference on Information and Automation, 2008, p.302-307 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Equations Information filters Mathematical model Message passing Particle filters Probability density function Robot sensing systems |
| title | Consensus based distributed particle filter in sensor networks |
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