Particle Swarm Optimization aided unscented kalman filter for ballistic target tracking
Tracking of a ballistic target in its reentry phase by considering the radar measurements is a highly complex problem in nonlinear filtering. Kalman Filter (KF) is used to estimate the positions of the target when the measurements are corrupted with noise. If the measurements (range and bearing) are...
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| creator | Jatoth, Ravi Kumar Rao, D Nagarjuna Kumar, K Sumanth |
| description | Tracking of a ballistic target in its reentry phase by considering the radar measurements is a highly complex problem in nonlinear filtering. Kalman Filter (KF) is used to estimate the positions of the target when the measurements are corrupted with noise. If the measurements (range and bearing) are nonlinear then Unscented Kalman filter (UKF) can be used. For obtaining reliable estimate of the target state, filter has to be tuned before the operation, which is offline. Tuning is the process of estimating the process noise covariance matrix (Q) and measurement noise covariance matrix (R) of the filter. This paper presents tuning of UKF using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for ballistic target tracking. Simulations results show that the superiority of PSO tuned UKF over conventional UKF. |
| doi_str_mv | 10.1109/ICCCCT.2010.5670595 |
| format | Conference Proceeding |
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Kalman Filter (KF) is used to estimate the positions of the target when the measurements are corrupted with noise. If the measurements (range and bearing) are nonlinear then Unscented Kalman filter (UKF) can be used. For obtaining reliable estimate of the target state, filter has to be tuned before the operation, which is offline. Tuning is the process of estimating the process noise covariance matrix (Q) and measurement noise covariance matrix (R) of the filter. This paper presents tuning of UKF using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for ballistic target tracking. Simulations results show that the superiority of PSO tuned UKF over conventional UKF.</description><identifier>ISBN: 9781424477692</identifier><identifier>ISBN: 1424477697</identifier><identifier>EISBN: 9781424477685</identifier><identifier>EISBN: 1424477700</identifier><identifier>EISBN: 1424477689</identifier><identifier>EISBN: 9781424477708</identifier><identifier>DOI: 10.1109/ICCCCT.2010.5670595</identifier><language>eng</language><publisher>IEEE</publisher><subject>ballistic target tracking ; Covariance matrix ; Gallium ; Genetic Algorithm ; Kalman filters ; Noise ; Particle swarm optimization ; Radar tracking ; Tuning ; Unscented Kalman filter</subject><ispartof>2010 INTERNATIONAL CONFERENCE ON COMMUNICATION CONTROL AND COMPUTING TECHNOLOGIES, 2010, p.455-460</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5670595$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,27904,54898</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5670595$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jatoth, Ravi Kumar</creatorcontrib><creatorcontrib>Rao, D Nagarjuna</creatorcontrib><creatorcontrib>Kumar, K Sumanth</creatorcontrib><title>Particle Swarm Optimization aided unscented kalman filter for ballistic target tracking</title><title>2010 INTERNATIONAL CONFERENCE ON COMMUNICATION CONTROL AND COMPUTING TECHNOLOGIES</title><addtitle>ICCCCT</addtitle><description>Tracking of a ballistic target in its reentry phase by considering the radar measurements is a highly complex problem in nonlinear filtering. Kalman Filter (KF) is used to estimate the positions of the target when the measurements are corrupted with noise. If the measurements (range and bearing) are nonlinear then Unscented Kalman filter (UKF) can be used. For obtaining reliable estimate of the target state, filter has to be tuned before the operation, which is offline. Tuning is the process of estimating the process noise covariance matrix (Q) and measurement noise covariance matrix (R) of the filter. This paper presents tuning of UKF using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for ballistic target tracking. Simulations results show that the superiority of PSO tuned UKF over conventional UKF.</description><subject>ballistic target tracking</subject><subject>Covariance matrix</subject><subject>Gallium</subject><subject>Genetic Algorithm</subject><subject>Kalman filters</subject><subject>Noise</subject><subject>Particle swarm optimization</subject><subject>Radar tracking</subject><subject>Tuning</subject><subject>Unscented Kalman filter</subject><isbn>9781424477692</isbn><isbn>1424477697</isbn><isbn>9781424477685</isbn><isbn>1424477700</isbn><isbn>1424477689</isbn><isbn>9781424477708</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkM1qwzAQhFVKoSX1E-SiF3CqX0s6FtOfQCCFGnoMK1kOamQnyCqlffoKmku-y-wuw8AsQktKVpQS87BuC92KkXKQjSLSyCtUGaWpYEIo1Wh5fbEbdouqef4kBcmUoOQOfbxBysFFj9-_IY14e8phDL-Qw3HCEHrf469pdn7KZTpAHGHCQ4jZJzwcE7YQY5hLAM6Q9j7jnMAdwrS_RzcDxNlXZ12g7vmpa1_rzfZl3T5u6mBIrrVRzCrgzvGe9cIpLr1m0g66l475hituhC9YcISDEcVnLLeljhm0o3yBlv-xoZh2pxRGSD-78zf4H6TvVR8</recordid><startdate>201010</startdate><enddate>201010</enddate><creator>Jatoth, Ravi Kumar</creator><creator>Rao, D Nagarjuna</creator><creator>Kumar, K Sumanth</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201010</creationdate><title>Particle Swarm Optimization aided unscented kalman filter for ballistic target tracking</title><author>Jatoth, Ravi Kumar ; Rao, D Nagarjuna ; Kumar, K Sumanth</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-8972b7a3cc3d2d4c735e825bf8d5c2e637394eeeebac03a94c3d9b3b4479f8c13</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>ballistic target tracking</topic><topic>Covariance matrix</topic><topic>Gallium</topic><topic>Genetic Algorithm</topic><topic>Kalman filters</topic><topic>Noise</topic><topic>Particle swarm optimization</topic><topic>Radar tracking</topic><topic>Tuning</topic><topic>Unscented Kalman filter</topic><toplevel>online_resources</toplevel><creatorcontrib>Jatoth, Ravi Kumar</creatorcontrib><creatorcontrib>Rao, D Nagarjuna</creatorcontrib><creatorcontrib>Kumar, K Sumanth</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</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>Jatoth, Ravi Kumar</au><au>Rao, D Nagarjuna</au><au>Kumar, K Sumanth</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Particle Swarm Optimization aided unscented kalman filter for ballistic target tracking</atitle><btitle>2010 INTERNATIONAL CONFERENCE ON COMMUNICATION CONTROL AND COMPUTING TECHNOLOGIES</btitle><stitle>ICCCCT</stitle><date>2010-10</date><risdate>2010</risdate><spage>455</spage><epage>460</epage><pages>455-460</pages><isbn>9781424477692</isbn><isbn>1424477697</isbn><eisbn>9781424477685</eisbn><eisbn>1424477700</eisbn><eisbn>1424477689</eisbn><eisbn>9781424477708</eisbn><abstract>Tracking of a ballistic target in its reentry phase by considering the radar measurements is a highly complex problem in nonlinear filtering. Kalman Filter (KF) is used to estimate the positions of the target when the measurements are corrupted with noise. If the measurements (range and bearing) are nonlinear then Unscented Kalman filter (UKF) can be used. For obtaining reliable estimate of the target state, filter has to be tuned before the operation, which is offline. Tuning is the process of estimating the process noise covariance matrix (Q) and measurement noise covariance matrix (R) of the filter. This paper presents tuning of UKF using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for ballistic target tracking. Simulations results show that the superiority of PSO tuned UKF over conventional UKF.</abstract><pub>IEEE</pub><doi>10.1109/ICCCCT.2010.5670595</doi><tpages>6</tpages></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | ballistic target tracking Covariance matrix Gallium Genetic Algorithm Kalman filters Noise Particle swarm optimization Radar tracking Tuning Unscented Kalman filter |
| title | Particle Swarm Optimization aided unscented kalman filter for ballistic target tracking |
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