Time-Frequency Analysis of Frequency Hopping Signals Based on Particle Swarm Optimization

A new method is proposed for blind parameter estimation of frequency hopping signals. According to the relation between peaks location on the time frequency plane and component centers of frequency hopping signals, parameter estimation problem is solved using multi-species particle swarm optimizatio...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Applied Mechanics and Materials 2012-08, Vol.195-196, p.265-269
1. Verfasser: Guo, Jian Tao
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 269
container_issue
container_start_page 265
container_title Applied Mechanics and Materials
container_volume 195-196
creator Guo, Jian Tao
description A new method is proposed for blind parameter estimation of frequency hopping signals. According to the relation between peaks location on the time frequency plane and component centers of frequency hopping signals, parameter estimation problem is solved using multi-species particle swarm optimization algorithm. Each particle moves around the time and frequency plane and will converge to different species, which species seed represents the center of frequency hopping component. Using this method, the parameters of frequency hopping signals can be estimated. Simulation results demonstrate that the method is effective and feasible.
doi_str_mv 10.4028/www.scientific.net/AMM.195-196.265
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_1442911905</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3102703751</sourcerecordid><originalsourceid>FETCH-LOGICAL-c368t-53f8a774a4cf0aa3f247775fb7ce47dd5d9b50d3914b6dbe26a29f3d4407717d3</originalsourceid><addsrcrecordid>eNqNkEtLAzEUhYMP0Fb_Q8CdMGMyySSTZX3UCkoFdeEqpJmkpnQeJiml_npTK9Sli8uFew7nHj4ALjHKKSqqq_V6nQftTBuddTpvTbwaPT3lWJQZFiwvWHkATjFjRcZpVRyCAUGEVyVlghz9CCgThLATMAhhgRCjmFan4P3VNSYbe_O5Mq3ewFGrlpvgAuws3F8nXd-7dg5f3DzpAV6rYGrYtfBZ-ej00sCXtfINnPbRNe5LRde1Z-DYJq85_91D8Da-e72ZZI_T-4eb0WOmCatiVhJbKc6potoipYgtKOe8tDOuDeV1XdZiVqKaCExnrJ6ZgqlCWFJTijjHvCZDcLHL7X2X6oYoF93Kb2tKTGkhMBaoTK7rnUv7LgRvrOy9a5TfSIzklq9MfOWer0x8ZeIrE980TCa-KeR2FxK9akM0-uPPr__HfAP0zoyx</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1442911905</pqid></control><display><type>article</type><title>Time-Frequency Analysis of Frequency Hopping Signals Based on Particle Swarm Optimization</title><source>Scientific.net Journals</source><creator>Guo, Jian Tao</creator><creatorcontrib>Guo, Jian Tao</creatorcontrib><description>A new method is proposed for blind parameter estimation of frequency hopping signals. According to the relation between peaks location on the time frequency plane and component centers of frequency hopping signals, parameter estimation problem is solved using multi-species particle swarm optimization algorithm. Each particle moves around the time and frequency plane and will converge to different species, which species seed represents the center of frequency hopping component. Using this method, the parameters of frequency hopping signals can be estimated. Simulation results demonstrate that the method is effective and feasible.</description><identifier>ISSN: 1660-9336</identifier><identifier>ISSN: 1662-7482</identifier><identifier>ISBN: 3037854693</identifier><identifier>ISBN: 9783037854693</identifier><identifier>EISSN: 1662-7482</identifier><identifier>DOI: 10.4028/www.scientific.net/AMM.195-196.265</identifier><language>eng</language><publisher>Zurich: Trans Tech Publications Ltd</publisher><ispartof>Applied Mechanics and Materials, 2012-08, Vol.195-196, p.265-269</ispartof><rights>2012 Trans Tech Publications Ltd</rights><rights>Copyright Trans Tech Publications Ltd. Aug 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-53f8a774a4cf0aa3f247775fb7ce47dd5d9b50d3914b6dbe26a29f3d4407717d3</citedby><cites>FETCH-LOGICAL-c368t-53f8a774a4cf0aa3f247775fb7ce47dd5d9b50d3914b6dbe26a29f3d4407717d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttps://www.scientific.net/Image/TitleCover/1935?width=600</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Guo, Jian Tao</creatorcontrib><title>Time-Frequency Analysis of Frequency Hopping Signals Based on Particle Swarm Optimization</title><title>Applied Mechanics and Materials</title><description>A new method is proposed for blind parameter estimation of frequency hopping signals. According to the relation between peaks location on the time frequency plane and component centers of frequency hopping signals, parameter estimation problem is solved using multi-species particle swarm optimization algorithm. Each particle moves around the time and frequency plane and will converge to different species, which species seed represents the center of frequency hopping component. Using this method, the parameters of frequency hopping signals can be estimated. Simulation results demonstrate that the method is effective and feasible.</description><issn>1660-9336</issn><issn>1662-7482</issn><issn>1662-7482</issn><isbn>3037854693</isbn><isbn>9783037854693</isbn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNqNkEtLAzEUhYMP0Fb_Q8CdMGMyySSTZX3UCkoFdeEqpJmkpnQeJiml_npTK9Sli8uFew7nHj4ALjHKKSqqq_V6nQftTBuddTpvTbwaPT3lWJQZFiwvWHkATjFjRcZpVRyCAUGEVyVlghz9CCgThLATMAhhgRCjmFan4P3VNSYbe_O5Mq3ewFGrlpvgAuws3F8nXd-7dg5f3DzpAV6rYGrYtfBZ-ej00sCXtfINnPbRNe5LRde1Z-DYJq85_91D8Da-e72ZZI_T-4eb0WOmCatiVhJbKc6potoipYgtKOe8tDOuDeV1XdZiVqKaCExnrJ6ZgqlCWFJTijjHvCZDcLHL7X2X6oYoF93Kb2tKTGkhMBaoTK7rnUv7LgRvrOy9a5TfSIzklq9MfOWer0x8ZeIrE980TCa-KeR2FxK9akM0-uPPr__HfAP0zoyx</recordid><startdate>20120801</startdate><enddate>20120801</enddate><creator>Guo, Jian Tao</creator><general>Trans Tech Publications Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>7TB</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BFMQW</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20120801</creationdate><title>Time-Frequency Analysis of Frequency Hopping Signals Based on Particle Swarm Optimization</title><author>Guo, Jian Tao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-53f8a774a4cf0aa3f247775fb7ce47dd5d9b50d3914b6dbe26a29f3d4407717d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guo, Jian Tao</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Continental Europe Database</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>Materials Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Materials Science Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Applied Mechanics and Materials</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Guo, Jian Tao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Time-Frequency Analysis of Frequency Hopping Signals Based on Particle Swarm Optimization</atitle><jtitle>Applied Mechanics and Materials</jtitle><date>2012-08-01</date><risdate>2012</risdate><volume>195-196</volume><spage>265</spage><epage>269</epage><pages>265-269</pages><issn>1660-9336</issn><issn>1662-7482</issn><eissn>1662-7482</eissn><isbn>3037854693</isbn><isbn>9783037854693</isbn><abstract>A new method is proposed for blind parameter estimation of frequency hopping signals. According to the relation between peaks location on the time frequency plane and component centers of frequency hopping signals, parameter estimation problem is solved using multi-species particle swarm optimization algorithm. Each particle moves around the time and frequency plane and will converge to different species, which species seed represents the center of frequency hopping component. Using this method, the parameters of frequency hopping signals can be estimated. Simulation results demonstrate that the method is effective and feasible.</abstract><cop>Zurich</cop><pub>Trans Tech Publications Ltd</pub><doi>10.4028/www.scientific.net/AMM.195-196.265</doi><tpages>5</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1660-9336
ispartof Applied Mechanics and Materials, 2012-08, Vol.195-196, p.265-269
issn 1660-9336
1662-7482
1662-7482
language eng
recordid cdi_proquest_journals_1442911905
source Scientific.net Journals
title Time-Frequency Analysis of Frequency Hopping Signals Based on Particle Swarm Optimization
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-05T03%3A01%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Time-Frequency%20Analysis%20of%20Frequency%20Hopping%20Signals%20Based%20on%20Particle%20Swarm%20Optimization&rft.jtitle=Applied%20Mechanics%20and%20Materials&rft.au=Guo,%20Jian%20Tao&rft.date=2012-08-01&rft.volume=195-196&rft.spage=265&rft.epage=269&rft.pages=265-269&rft.issn=1660-9336&rft.eissn=1662-7482&rft.isbn=3037854693&rft.isbn_list=9783037854693&rft_id=info:doi/10.4028/www.scientific.net/AMM.195-196.265&rft_dat=%3Cproquest_cross%3E3102703751%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1442911905&rft_id=info:pmid/&rfr_iscdi=true