Loading…
Fixed-Lag Smoothing for Bayes Optimal Knowledge Exploitation in Target Tracking
In this work, we are interested in the improvements attainable when multiscan processing of external knowledge is performed over a moving time window. We propose a novel algorithm that enforces the state constraints by using a Fixed-Lag Smoothing procedure within the prediction step of the Bayesian...
Saved in:
| Published in: | IEEE transactions on signal processing 2014-06, Vol.62 (12), p.3143-3152 |
|---|---|
| Main Authors: | , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c321t-ec21655094013755ceb914349677df90aee90305d956d47142f9c5b7070d9b13 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c321t-ec21655094013755ceb914349677df90aee90305d956d47142f9c5b7070d9b13 |
| container_end_page | 3152 |
| container_issue | 12 |
| container_start_page | 3143 |
| container_title | IEEE transactions on signal processing |
| container_volume | 62 |
| creator | Papi, Francesco Bocquel, Melanie Podt, Martin Boers, Yvo |
| description | In this work, we are interested in the improvements attainable when multiscan processing of external knowledge is performed over a moving time window. We propose a novel algorithm that enforces the state constraints by using a Fixed-Lag Smoothing procedure within the prediction step of the Bayesian recursion. For proving the improvements, we utilize differential entropy as a measure of uncertainty and show that the approach guarantees a lower or equal posterior differential entropy than classical single-step constrained filtering. Simulation results using examples for single-target tracking are presented to verify that a Sequential Monte Carlo implementation of the proposed algorithm guarantees an improved tracking accuracy. |
| doi_str_mv | 10.1109/TSP.2014.2321731 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_pasca</sourceid><recordid>TN_cdi_pascalfrancis_primary_28604178</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6810172</ieee_id><sourcerecordid>3443544261</sourcerecordid><originalsourceid>FETCH-LOGICAL-c321t-ec21655094013755ceb914349677df90aee90305d956d47142f9c5b7070d9b13</originalsourceid><addsrcrecordid>eNo9kNFLwzAQxosoOKfvgi8B8bHzLk2a5lFlU3EwYX3wrWRtWjO7ZiYdbv-9GRt7uoP7fd_dfVF0izBCBPmYzz9HFJCNaEJRJHgWDVAyjIGJ9Dz0wJOYZ-LrMrryfgmBZDIdRLOJ2eoqnqqGzFfW9t-ma0htHXlWO-3JbN2blWrJR2f_Wl01moy369aaXvXGdsR0JFeu0T3JnSp_gvY6uqhV6_XNsQ6jfDLOX97i6ez1_eVpGpfhvD7WJcWUc5AMMBGcl3ohkSXhJCGqWoLSWkICvJI8rZhARmtZ8oUAAZVcYDKM7g-2a2d_N9r3xdJuXBc2FsiDMZVSsEDBgSqd9d7puli78I7bFQjFPrUipFbsUyuOqQXJw9FY-VK1tVNdafxJR7MUGIoscHcHzmitT-M0Q0BBk3-6PHOU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1565529974</pqid></control><display><type>article</type><title>Fixed-Lag Smoothing for Bayes Optimal Knowledge Exploitation in Target Tracking</title><source>IEEE Electronic Library (IEL) Journals</source><creator>Papi, Francesco ; Bocquel, Melanie ; Podt, Martin ; Boers, Yvo</creator><creatorcontrib>Papi, Francesco ; Bocquel, Melanie ; Podt, Martin ; Boers, Yvo</creatorcontrib><description>In this work, we are interested in the improvements attainable when multiscan processing of external knowledge is performed over a moving time window. We propose a novel algorithm that enforces the state constraints by using a Fixed-Lag Smoothing procedure within the prediction step of the Bayesian recursion. For proving the improvements, we utilize differential entropy as a measure of uncertainty and show that the approach guarantees a lower or equal posterior differential entropy than classical single-step constrained filtering. Simulation results using examples for single-target tracking are presented to verify that a Sequential Monte Carlo implementation of the proposed algorithm guarantees an improved tracking accuracy.</description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/TSP.2014.2321731</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Applied sciences ; Approximation methods ; Bayes methods ; constrained filtering ; Detection, estimation, filtering, equalization, prediction ; differential entropy ; Entropy ; Exact sciences and technology ; External knowledge ; fixed-lag smoothing ; Information, signal and communications theory ; Radar tracking ; sequential Monte Carlo ; Signal and communications theory ; Signal, noise ; Smoothing methods ; Target tracking ; Telecommunications and information theory ; Uncertainty</subject><ispartof>IEEE transactions on signal processing, 2014-06, Vol.62 (12), p.3143-3152</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c321t-ec21655094013755ceb914349677df90aee90305d956d47142f9c5b7070d9b13</citedby><cites>FETCH-LOGICAL-c321t-ec21655094013755ceb914349677df90aee90305d956d47142f9c5b7070d9b13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6810172$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,54796</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28604178$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Papi, Francesco</creatorcontrib><creatorcontrib>Bocquel, Melanie</creatorcontrib><creatorcontrib>Podt, Martin</creatorcontrib><creatorcontrib>Boers, Yvo</creatorcontrib><title>Fixed-Lag Smoothing for Bayes Optimal Knowledge Exploitation in Target Tracking</title><title>IEEE transactions on signal processing</title><addtitle>TSP</addtitle><description>In this work, we are interested in the improvements attainable when multiscan processing of external knowledge is performed over a moving time window. We propose a novel algorithm that enforces the state constraints by using a Fixed-Lag Smoothing procedure within the prediction step of the Bayesian recursion. For proving the improvements, we utilize differential entropy as a measure of uncertainty and show that the approach guarantees a lower or equal posterior differential entropy than classical single-step constrained filtering. Simulation results using examples for single-target tracking are presented to verify that a Sequential Monte Carlo implementation of the proposed algorithm guarantees an improved tracking accuracy.</description><subject>Applied sciences</subject><subject>Approximation methods</subject><subject>Bayes methods</subject><subject>constrained filtering</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>differential entropy</subject><subject>Entropy</subject><subject>Exact sciences and technology</subject><subject>External knowledge</subject><subject>fixed-lag smoothing</subject><subject>Information, signal and communications theory</subject><subject>Radar tracking</subject><subject>sequential Monte Carlo</subject><subject>Signal and communications theory</subject><subject>Signal, noise</subject><subject>Smoothing methods</subject><subject>Target tracking</subject><subject>Telecommunications and information theory</subject><subject>Uncertainty</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNo9kNFLwzAQxosoOKfvgi8B8bHzLk2a5lFlU3EwYX3wrWRtWjO7ZiYdbv-9GRt7uoP7fd_dfVF0izBCBPmYzz9HFJCNaEJRJHgWDVAyjIGJ9Dz0wJOYZ-LrMrryfgmBZDIdRLOJ2eoqnqqGzFfW9t-ma0htHXlWO-3JbN2blWrJR2f_Wl01moy369aaXvXGdsR0JFeu0T3JnSp_gvY6uqhV6_XNsQ6jfDLOX97i6ez1_eVpGpfhvD7WJcWUc5AMMBGcl3ohkSXhJCGqWoLSWkICvJI8rZhARmtZ8oUAAZVcYDKM7g-2a2d_N9r3xdJuXBc2FsiDMZVSsEDBgSqd9d7puli78I7bFQjFPrUipFbsUyuOqQXJw9FY-VK1tVNdafxJR7MUGIoscHcHzmitT-M0Q0BBk3-6PHOU</recordid><startdate>20140615</startdate><enddate>20140615</enddate><creator>Papi, Francesco</creator><creator>Bocquel, Melanie</creator><creator>Podt, Martin</creator><creator>Boers, Yvo</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20140615</creationdate><title>Fixed-Lag Smoothing for Bayes Optimal Knowledge Exploitation in Target Tracking</title><author>Papi, Francesco ; Bocquel, Melanie ; Podt, Martin ; Boers, Yvo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c321t-ec21655094013755ceb914349677df90aee90305d956d47142f9c5b7070d9b13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Applied sciences</topic><topic>Approximation methods</topic><topic>Bayes methods</topic><topic>constrained filtering</topic><topic>Detection, estimation, filtering, equalization, prediction</topic><topic>differential entropy</topic><topic>Entropy</topic><topic>Exact sciences and technology</topic><topic>External knowledge</topic><topic>fixed-lag smoothing</topic><topic>Information, signal and communications theory</topic><topic>Radar tracking</topic><topic>sequential Monte Carlo</topic><topic>Signal and communications theory</topic><topic>Signal, noise</topic><topic>Smoothing methods</topic><topic>Target tracking</topic><topic>Telecommunications and information theory</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Papi, Francesco</creatorcontrib><creatorcontrib>Bocquel, Melanie</creatorcontrib><creatorcontrib>Podt, Martin</creatorcontrib><creatorcontrib>Boers, Yvo</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library Online</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Papi, Francesco</au><au>Bocquel, Melanie</au><au>Podt, Martin</au><au>Boers, Yvo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fixed-Lag Smoothing for Bayes Optimal Knowledge Exploitation in Target Tracking</atitle><jtitle>IEEE transactions on signal processing</jtitle><stitle>TSP</stitle><date>2014-06-15</date><risdate>2014</risdate><volume>62</volume><issue>12</issue><spage>3143</spage><epage>3152</epage><pages>3143-3152</pages><issn>1053-587X</issn><eissn>1941-0476</eissn><coden>ITPRED</coden><abstract>In this work, we are interested in the improvements attainable when multiscan processing of external knowledge is performed over a moving time window. We propose a novel algorithm that enforces the state constraints by using a Fixed-Lag Smoothing procedure within the prediction step of the Bayesian recursion. For proving the improvements, we utilize differential entropy as a measure of uncertainty and show that the approach guarantees a lower or equal posterior differential entropy than classical single-step constrained filtering. Simulation results using examples for single-target tracking are presented to verify that a Sequential Monte Carlo implementation of the proposed algorithm guarantees an improved tracking accuracy.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TSP.2014.2321731</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1053-587X |
| ispartof | IEEE transactions on signal processing, 2014-06, Vol.62 (12), p.3143-3152 |
| issn | 1053-587X 1941-0476 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_28604178 |
| source | IEEE Electronic Library (IEL) Journals |
| subjects | Applied sciences Approximation methods Bayes methods constrained filtering Detection, estimation, filtering, equalization, prediction differential entropy Entropy Exact sciences and technology External knowledge fixed-lag smoothing Information, signal and communications theory Radar tracking sequential Monte Carlo Signal and communications theory Signal, noise Smoothing methods Target tracking Telecommunications and information theory Uncertainty |
| title | Fixed-Lag Smoothing for Bayes Optimal Knowledge Exploitation in Target Tracking |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T19%3A53%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pasca&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Fixed-Lag%20Smoothing%20for%20Bayes%20Optimal%20Knowledge%20Exploitation%20in%20Target%20Tracking&rft.jtitle=IEEE%20transactions%20on%20signal%20processing&rft.au=Papi,%20Francesco&rft.date=2014-06-15&rft.volume=62&rft.issue=12&rft.spage=3143&rft.epage=3152&rft.pages=3143-3152&rft.issn=1053-587X&rft.eissn=1941-0476&rft.coden=ITPRED&rft_id=info:doi/10.1109/TSP.2014.2321731&rft_dat=%3Cproquest_pasca%3E3443544261%3C/proquest_pasca%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c321t-ec21655094013755ceb914349677df90aee90305d956d47142f9c5b7070d9b13%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1565529974&rft_id=info:pmid/&rft_ieee_id=6810172&rfr_iscdi=true |