改进cell密度聚类算法在空战目标分群中的应用

V247; 针对传统聚类算法对流形分布数据聚类效果差,且实时性不高的缺点,提出改进基于cell的密度聚类(Cell-Based density Spatial Clustering of Applications with Noise,CBSCAN)算法解决实时空战目标分群问题.通过分析空战态势参数,建立了空战目标分群通用模型,将目标分群转化为聚类问题.通过改进CBSCAN算法的簇类扩展方式,建立基于改进CBSCAN算法的目标分群模型.通过仿真实验,对比分析了K-means、最大期望算法、密度峰值算法、密度聚类算法、CBSCAN算法和改进CBSCAN算法在30种作战态势下的分群准确性和实时性,...

Full description

Saved in:
Bibliographic Details
Published in国防科技大学学报 Vol. 43; no. 4; pp. 108 - 117
Main Authors 闫孟达, 杨任农, 王新, 左家亮, 嵇慧明, 尚金祥
Format Journal Article
LanguageChinese
Published 空军工程大学空管领航学院,陕西西安 710051%中国人民解放军94994部队,江苏南京 210019%中国人民解放军94701部队,安徽安庆 246000%中国人民解放军94347部队,辽宁沈阳 110042 28.08.2021
Subjects
Online AccessGet full text
ISSN1001-2486
DOI10.11887/j.cn.202104014

Cover

Abstract V247; 针对传统聚类算法对流形分布数据聚类效果差,且实时性不高的缺点,提出改进基于cell的密度聚类(Cell-Based density Spatial Clustering of Applications with Noise,CBSCAN)算法解决实时空战目标分群问题.通过分析空战态势参数,建立了空战目标分群通用模型,将目标分群转化为聚类问题.通过改进CBSCAN算法的簇类扩展方式,建立基于改进CBSCAN算法的目标分群模型.通过仿真实验,对比分析了K-means、最大期望算法、密度峰值算法、密度聚类算法、CBSCAN算法和改进CBSCAN算法在30种作战态势下的分群准确性和实时性,结果表明:改进CBSCAN算法可以在编队数目未知和目标流形分布的条件下,对多目标编队进行正确分群,且实时性较原始算法提高约30%,具有实际应用价值.
AbstractList V247; 针对传统聚类算法对流形分布数据聚类效果差,且实时性不高的缺点,提出改进基于cell的密度聚类(Cell-Based density Spatial Clustering of Applications with Noise,CBSCAN)算法解决实时空战目标分群问题.通过分析空战态势参数,建立了空战目标分群通用模型,将目标分群转化为聚类问题.通过改进CBSCAN算法的簇类扩展方式,建立基于改进CBSCAN算法的目标分群模型.通过仿真实验,对比分析了K-means、最大期望算法、密度峰值算法、密度聚类算法、CBSCAN算法和改进CBSCAN算法在30种作战态势下的分群准确性和实时性,结果表明:改进CBSCAN算法可以在编队数目未知和目标流形分布的条件下,对多目标编队进行正确分群,且实时性较原始算法提高约30%,具有实际应用价值.
Author 王新
闫孟达
嵇慧明
左家亮
尚金祥
杨任农
AuthorAffiliation 空军工程大学空管领航学院,陕西西安 710051%中国人民解放军94994部队,江苏南京 210019%中国人民解放军94701部队,安徽安庆 246000%中国人民解放军94347部队,辽宁沈阳 110042
AuthorAffiliation_xml – name: 空军工程大学空管领航学院,陕西西安 710051%中国人民解放军94994部队,江苏南京 210019%中国人民解放军94701部队,安徽安庆 246000%中国人民解放军94347部队,辽宁沈阳 110042
Author_FL YANG Rennong
YAN Mengda
SHANG Jinxiang
WANG Xin
JI Huiming
ZUO Jialiang
Author_FL_xml – sequence: 1
  fullname: YAN Mengda
– sequence: 2
  fullname: YANG Rennong
– sequence: 3
  fullname: WANG Xin
– sequence: 4
  fullname: ZUO Jialiang
– sequence: 5
  fullname: JI Huiming
– sequence: 6
  fullname: SHANG Jinxiang
Author_xml – sequence: 1
  fullname: 闫孟达
– sequence: 2
  fullname: 杨任农
– sequence: 3
  fullname: 王新
– sequence: 4
  fullname: 左家亮
– sequence: 5
  fullname: 嵇慧明
– sequence: 6
  fullname: 尚金祥
BookMark eNo9j7tKA0EYRqeIYIypfQaLXeefnZ2ZFBYSvEHARuswO5dgXDbgIqYVJOYBVgyJhYVILIyIja6gL-Pu5DEMKFZfd875VlAl6SUGoTXAPoAQfKPrq8QnmACmGGgFVQFj8AgVbBnV0_Q4wiQAxoFDFW2W2fv8a6JMHBfPgyJ_mF-M3cuHm92Ur9fF7dQ95uVw5Caz8u6qGA7c5_3325MbXxZ55rLpKlqyMk5N_W9r6Ghn-7C557UOdvebWy0vXTRQjxgODQgAOJM6tIJHSkuDNTGGSWJCpgjVEDWUlUJRirm1IZMB0yBDYrQIamj9l3suEyuTTrvbOztNFsZ2x550db8f_d8NfgBmY2Go
ClassificationCodes V247
ContentType Journal Article
Copyright Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
Copyright_xml – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
DBID 2B.
4A8
92I
93N
PSX
TCJ
DOI 10.11887/j.cn.202104014
DatabaseName Wanfang Data Journals - Hong Kong
WANFANG Data Centre
Wanfang Data Journals
万方数据期刊 - 香港版
China Online Journals (COJ)
China Online Journals (COJ)
DatabaseTitleList
DeliveryMethod fulltext_linktorsrc
Discipline Military & Naval Science
DocumentTitle_FL Air combat target grouping based on improved CBSCAN algorithm
EndPage 117
ExternalDocumentID gfkjdxxb202104014
GrantInformation_xml – fundername: (国家自然科学基金资助项目); (国家社会科学基金资助项目)
  funderid: (国家自然科学基金资助项目); (国家社会科学基金资助项目)
GroupedDBID -03
2B.
4A8
5XA
5XD
92H
92I
93N
ABJNI
ACGFS
ALMA_UNASSIGNED_HOLDINGS
CCEZO
CEKLB
CW9
PSX
TCJ
TGT
TN5
U1G
U5M
ID FETCH-LOGICAL-s1044-2e719131176ad5f87bcdae0d2ee6a2e56c24d1b9cfa8c4407ff56a36d1a52ed83
ISSN 1001-2486
IngestDate Thu May 29 04:04:51 EDT 2025
IsPeerReviewed false
IsScholarly true
Issue 4
Keywords 态势感知;目标分群;多编队协同空战;流形分布;改进CBSCAN算法
Language Chinese
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-s1044-2e719131176ad5f87bcdae0d2ee6a2e56c24d1b9cfa8c4407ff56a36d1a52ed83
PageCount 10
ParticipantIDs wanfang_journals_gfkjdxxb202104014
PublicationCentury 2000
PublicationDate 2021-08-28
PublicationDateYYYYMMDD 2021-08-28
PublicationDate_xml – month: 08
  year: 2021
  text: 2021-08-28
  day: 28
PublicationDecade 2020
PublicationTitle 国防科技大学学报
PublicationTitle_FL Journal of National University of Defense Technology
PublicationYear 2021
Publisher 空军工程大学空管领航学院,陕西西安 710051%中国人民解放军94994部队,江苏南京 210019%中国人民解放军94701部队,安徽安庆 246000%中国人民解放军94347部队,辽宁沈阳 110042
Publisher_xml – name: 空军工程大学空管领航学院,陕西西安 710051%中国人民解放军94994部队,江苏南京 210019%中国人民解放军94701部队,安徽安庆 246000%中国人民解放军94347部队,辽宁沈阳 110042
SSID ssib023167171
ssib057620141
ssib051370975
ssib001129263
ssj0000556656
Score 2.3513772
Snippet V247; 针对传统聚类算法对流形分布数据聚类效果差,且实时性不高的缺点,提出改进基于cell的密度聚类(Cell-Based density Spatial Clustering of Applications with...
SourceID wanfang
SourceType Aggregation Database
StartPage 108
Title 改进cell密度聚类算法在空战目标分群中的应用
URI https://d.wanfangdata.com.cn/periodical/gfkjdxxb202104014
Volume 43
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVEBS
  databaseName: Inspec with Full Text
  issn: 1001-2486
  databaseCode: ADMLS
  dateStart: 20181028
  customDbUrl:
  isFulltext: true
  dateEnd: 99991231
  titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text
  omitProxy: false
  ssIdentifier: ssib057620141
  providerName: EBSCOhost
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NaxQxFB9qe_EifuI3ixgvZTWTyWSSg4dkd5Yithdb6K1kZ2cVhQr9gNqbILV_QEVpPXgQqQcr4kUr6D_jdvtn-N7b2d2pVVARliEkv33fZF5C8iYIrma6qQT3ssojz6tStZrVpm-bquGZzL1X-HUcPG0xpSZm5K3ZeHZkTJdOLS0vNa9nq7-8V_IvXoU-8Cvekv0Lzw6IQge0wb_wBA_D8498zFLFjGTOsFQz12DG4T48S2NmG0wrbDjLrMJhHTJjWZowFzLnsGFTZhIk4SJmYgSbGrOahgz-EYa0ZoZ6jEM89FjOdIJgGEIWQDBlVrIU5NDM1glsmZYFd5APeyRQLifCxM4xB3iDLJwgvoAMia_FIxioiMRObNRJkUGDMDbuBwyRAQKugJgG2SSF3xAC1qqThhJN4AgLOpjaEJIwnTLtCAuW4cMR0CbpiwBkFVEBEdLyxomgneDiIjqGesmaPV6uTymmIU3MDus5-Bf5yYaonxVkcY2mt7YEBtVpVNSKNrpTIwvXKDVIcG3GsdRSDG-wuOS0gTdIqZ7zHSdvaJTMRv1gSweaYMUhiRx1RFYlR4LhUQyFcdZzAiioibuuU8D1zFYbF1iYy_wPMRIe_kaMQmWyeUqEyz0QZGpcSMjG-f8QI5LJb8TQB7lTiEPE6z7MReNUVLG0B4_GqQpZVHAvkoherbFispSljCDkupRchr2LyofzFo1bf5C4ZFiTGewvee9u9U_F4O-2H9xvraw0B5gjwZhIlBKjwZitT96-M1wKQSIvhqX1BFa8CIdLvziMEm6GS4cYkiI88z3Yn8YiW4o-fj3Qt6iMhrLeOCgp3Xicb_v5u6XkfPp4cKxYVVdsb4o8EYys3jsZnJ2kLxAsPKpcq0x5mHcrRSJzKri5t_F5_9sWzpad92ud3Tf7jze7H750d57vfXzWebndfbu7t_6iu7Wz9-ppZ32t-_X190_vuptPOrsb3Y3t08FMI52uTVSLD8lUF0E-WRV5EhqsK5Yo34rbOmlmLZ_zlshz5UUeq0zIVtg0WdvrTEqetNux8pFqhT4WeUtHZ4LR-Yfz-dmg4mWe-DyDvN8ImWfa5LjE9iLhMfdRzs8FVwpLzBUvisW5Q047_yegC8HR4ax1MRhdWljOL8ECaKl5ufD1D2sQ860
linkProvider EBSCOhost
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E6%94%B9%E8%BF%9Bcell%E5%AF%86%E5%BA%A6%E8%81%9A%E7%B1%BB%E7%AE%97%E6%B3%95%E5%9C%A8%E7%A9%BA%E6%88%98%E7%9B%AE%E6%A0%87%E5%88%86%E7%BE%A4%E4%B8%AD%E7%9A%84%E5%BA%94%E7%94%A8&rft.jtitle=%E5%9B%BD%E9%98%B2%E7%A7%91%E6%8A%80%E5%A4%A7%E5%AD%A6%E5%AD%A6%E6%8A%A5&rft.au=%E9%97%AB%E5%AD%9F%E8%BE%BE&rft.au=%E6%9D%A8%E4%BB%BB%E5%86%9C&rft.au=%E7%8E%8B%E6%96%B0&rft.au=%E5%B7%A6%E5%AE%B6%E4%BA%AE&rft.date=2021-08-28&rft.pub=%E7%A9%BA%E5%86%9B%E5%B7%A5%E7%A8%8B%E5%A4%A7%E5%AD%A6%E7%A9%BA%E7%AE%A1%E9%A2%86%E8%88%AA%E5%AD%A6%E9%99%A2%2C%E9%99%95%E8%A5%BF%E8%A5%BF%E5%AE%89+710051%25%E4%B8%AD%E5%9B%BD%E4%BA%BA%E6%B0%91%E8%A7%A3%E6%94%BE%E5%86%9B94994%E9%83%A8%E9%98%9F%2C%E6%B1%9F%E8%8B%8F%E5%8D%97%E4%BA%AC+210019%25%E4%B8%AD%E5%9B%BD%E4%BA%BA%E6%B0%91%E8%A7%A3%E6%94%BE%E5%86%9B94701%E9%83%A8%E9%98%9F%2C%E5%AE%89%E5%BE%BD%E5%AE%89%E5%BA%86+246000%25%E4%B8%AD%E5%9B%BD%E4%BA%BA%E6%B0%91%E8%A7%A3%E6%94%BE%E5%86%9B94347%E9%83%A8%E9%98%9F%2C%E8%BE%BD%E5%AE%81%E6%B2%88%E9%98%B3+110042&rft.issn=1001-2486&rft.volume=43&rft.issue=4&rft.spage=108&rft.epage=117&rft_id=info:doi/10.11887%2Fj.cn.202104014&rft.externalDocID=gfkjdxxb202104014
thumbnail_s http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fgfkjdxxb%2Fgfkjdxxb.jpg