Track fusion based on threshold factor classification algorithm in wireless sensor networks
Summary Traditional tracking classification algorithm has been widely applied to target tracking in wireless sensor networks. In this paper, focusing on the accuracy of target tracking in wireless sensor networks, we propose an improved threshold factor track classification algorithm. The algorithm...
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| Veröffentlicht in: | International journal of communication systems 2017-05, Vol.30 (7), p.np-n/a |
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| container_issue | 7 |
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| container_title | International journal of communication systems |
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| creator | Wang, Xiang Wang, Tao Chen, Shiyang Fan, Renhao Xu, Yang Wang, Weike Li, Hongge Xia, Tongsheng |
| description | Summary
Traditional tracking classification algorithm has been widely applied to target tracking in wireless sensor networks. In this paper, focusing on the accuracy of target tracking in wireless sensor networks, we propose an improved threshold factor track classification algorithm. The algorithm extracts the motion model according to the intrinsic properties of the target. It updates the iterative center according to the real‐time motion state of the moving target and timely filters out the weak correlated or uncorrelated data. In order to show the improved threshold factor classification algorithm is more effective, we compare the proposed algorithm with the classification algorithm based on the Euclidean distance comprehensive function. Experimental results show that through the proposed algorithm, the mean error and variance in the direction of x/y/z have been reduced to a certain extent, and the computation time consumed is also reduced. Copyright © 2016 John Wiley & Sons, Ltd.
Focusing on the accuracy of target tracking in wireless sensor networks, we propose an improved threshold factor track classification algorithm. The algorithm extracts the motion model according to the intrinsic properties of the target. It updates the iterative center according to the real‐time motion state of the moving target and timely filters out the weak correlated or uncorrelated data. |
| doi_str_mv | 10.1002/dac.3164 |
| format | Article |
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Traditional tracking classification algorithm has been widely applied to target tracking in wireless sensor networks. In this paper, focusing on the accuracy of target tracking in wireless sensor networks, we propose an improved threshold factor track classification algorithm. The algorithm extracts the motion model according to the intrinsic properties of the target. It updates the iterative center according to the real‐time motion state of the moving target and timely filters out the weak correlated or uncorrelated data. In order to show the improved threshold factor classification algorithm is more effective, we compare the proposed algorithm with the classification algorithm based on the Euclidean distance comprehensive function. Experimental results show that through the proposed algorithm, the mean error and variance in the direction of x/y/z have been reduced to a certain extent, and the computation time consumed is also reduced. Copyright © 2016 John Wiley & Sons, Ltd.
Focusing on the accuracy of target tracking in wireless sensor networks, we propose an improved threshold factor track classification algorithm. The algorithm extracts the motion model according to the intrinsic properties of the target. It updates the iterative center according to the real‐time motion state of the moving target and timely filters out the weak correlated or uncorrelated data.</description><identifier>ISSN: 1074-5351</identifier><identifier>EISSN: 1099-1131</identifier><identifier>DOI: 10.1002/dac.3164</identifier><language>eng</language><publisher>Chichester: Wiley Subscription Services, Inc</publisher><subject>Algorithms ; Classification ; computation time ; improved threshold factor classification algorithm ; Mathematical models ; motion model ; Remote sensors ; Target tracking ; Thresholds ; Tracking ; Wireless networks ; wireless sensor networks</subject><ispartof>International journal of communication systems, 2017-05, Vol.30 (7), p.np-n/a</ispartof><rights>Copyright © 2016 John Wiley & Sons, Ltd.</rights><rights>Copyright © 2017 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3264-a692c1474d85e0d21aa6ade9bfc8c07321461891832b87cbbf4c768c1b593c23</citedby><cites>FETCH-LOGICAL-c3264-a692c1474d85e0d21aa6ade9bfc8c07321461891832b87cbbf4c768c1b593c23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fdac.3164$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fdac.3164$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>Wang, Xiang</creatorcontrib><creatorcontrib>Wang, Tao</creatorcontrib><creatorcontrib>Chen, Shiyang</creatorcontrib><creatorcontrib>Fan, Renhao</creatorcontrib><creatorcontrib>Xu, Yang</creatorcontrib><creatorcontrib>Wang, Weike</creatorcontrib><creatorcontrib>Li, Hongge</creatorcontrib><creatorcontrib>Xia, Tongsheng</creatorcontrib><title>Track fusion based on threshold factor classification algorithm in wireless sensor networks</title><title>International journal of communication systems</title><description>Summary
Traditional tracking classification algorithm has been widely applied to target tracking in wireless sensor networks. In this paper, focusing on the accuracy of target tracking in wireless sensor networks, we propose an improved threshold factor track classification algorithm. The algorithm extracts the motion model according to the intrinsic properties of the target. It updates the iterative center according to the real‐time motion state of the moving target and timely filters out the weak correlated or uncorrelated data. In order to show the improved threshold factor classification algorithm is more effective, we compare the proposed algorithm with the classification algorithm based on the Euclidean distance comprehensive function. Experimental results show that through the proposed algorithm, the mean error and variance in the direction of x/y/z have been reduced to a certain extent, and the computation time consumed is also reduced. Copyright © 2016 John Wiley & Sons, Ltd.
Focusing on the accuracy of target tracking in wireless sensor networks, we propose an improved threshold factor track classification algorithm. The algorithm extracts the motion model according to the intrinsic properties of the target. It updates the iterative center according to the real‐time motion state of the moving target and timely filters out the weak correlated or uncorrelated data.</description><subject>Algorithms</subject><subject>Classification</subject><subject>computation time</subject><subject>improved threshold factor classification algorithm</subject><subject>Mathematical models</subject><subject>motion model</subject><subject>Remote sensors</subject><subject>Target tracking</subject><subject>Thresholds</subject><subject>Tracking</subject><subject>Wireless networks</subject><subject>wireless sensor networks</subject><issn>1074-5351</issn><issn>1099-1131</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp10E1LAzEQBuAgCtYq-BMWvHjZmkn2IzmW-gmCl948LNls1qZNNzWzS-m_N2sFQfA07-GZYXgJuQY6A0rZXaP0jEORnZAJUClTAA6nYy6zNOc5nJMLxDWlVLAin5D3ZVB6k7QDWt8ltULTJDH0q2Bw5V2TtEr3PiTaKUTbWq36ESr34YPtV9vEdsneBuMMYoKmw2g70-992OAlOWuVQ3P1M6dk-fiwXDynr29PL4v5a6o5K7JUFZJpyMqsEbmhDQOlCtUYWbdaaFpyBlkBQoLgrBalrus202UhNNS55JrxKbk9nt0F_zkY7KutRW2cU53xA1Zxl0ugDGSkN3_o2g-hi89FJWgsUEr5e1AHjxhMW-2C3apwqIBWY8lVLLkaS440PdK9debwr6vu54tv_wXS_X13</recordid><startdate>20170510</startdate><enddate>20170510</enddate><creator>Wang, Xiang</creator><creator>Wang, Tao</creator><creator>Chen, Shiyang</creator><creator>Fan, Renhao</creator><creator>Xu, Yang</creator><creator>Wang, Weike</creator><creator>Li, Hongge</creator><creator>Xia, Tongsheng</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope></search><sort><creationdate>20170510</creationdate><title>Track fusion based on threshold factor classification algorithm in wireless sensor networks</title><author>Wang, Xiang ; Wang, Tao ; Chen, Shiyang ; Fan, Renhao ; Xu, Yang ; Wang, Weike ; Li, Hongge ; Xia, Tongsheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3264-a692c1474d85e0d21aa6ade9bfc8c07321461891832b87cbbf4c768c1b593c23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>Classification</topic><topic>computation time</topic><topic>improved threshold factor classification algorithm</topic><topic>Mathematical models</topic><topic>motion model</topic><topic>Remote sensors</topic><topic>Target tracking</topic><topic>Thresholds</topic><topic>Tracking</topic><topic>Wireless networks</topic><topic>wireless sensor networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Xiang</creatorcontrib><creatorcontrib>Wang, Tao</creatorcontrib><creatorcontrib>Chen, Shiyang</creatorcontrib><creatorcontrib>Fan, Renhao</creatorcontrib><creatorcontrib>Xu, Yang</creatorcontrib><creatorcontrib>Wang, Weike</creatorcontrib><creatorcontrib>Li, Hongge</creatorcontrib><creatorcontrib>Xia, Tongsheng</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>International journal of communication systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Xiang</au><au>Wang, Tao</au><au>Chen, Shiyang</au><au>Fan, Renhao</au><au>Xu, Yang</au><au>Wang, Weike</au><au>Li, Hongge</au><au>Xia, Tongsheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Track fusion based on threshold factor classification algorithm in wireless sensor networks</atitle><jtitle>International journal of communication systems</jtitle><date>2017-05-10</date><risdate>2017</risdate><volume>30</volume><issue>7</issue><spage>np</spage><epage>n/a</epage><pages>np-n/a</pages><issn>1074-5351</issn><eissn>1099-1131</eissn><abstract>Summary
Traditional tracking classification algorithm has been widely applied to target tracking in wireless sensor networks. In this paper, focusing on the accuracy of target tracking in wireless sensor networks, we propose an improved threshold factor track classification algorithm. The algorithm extracts the motion model according to the intrinsic properties of the target. It updates the iterative center according to the real‐time motion state of the moving target and timely filters out the weak correlated or uncorrelated data. In order to show the improved threshold factor classification algorithm is more effective, we compare the proposed algorithm with the classification algorithm based on the Euclidean distance comprehensive function. Experimental results show that through the proposed algorithm, the mean error and variance in the direction of x/y/z have been reduced to a certain extent, and the computation time consumed is also reduced. Copyright © 2016 John Wiley & Sons, Ltd.
Focusing on the accuracy of target tracking in wireless sensor networks, we propose an improved threshold factor track classification algorithm. The algorithm extracts the motion model according to the intrinsic properties of the target. It updates the iterative center according to the real‐time motion state of the moving target and timely filters out the weak correlated or uncorrelated data.</abstract><cop>Chichester</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/dac.3164</doi><tpages>15</tpages></addata></record> |
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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Algorithms Classification computation time improved threshold factor classification algorithm Mathematical models motion model Remote sensors Target tracking Thresholds Tracking Wireless networks wireless sensor networks |
| title | Track fusion based on threshold factor classification algorithm in wireless sensor networks |
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