A Segmentation-Based CFAR Detector With Spatial Continuity Constraint in Nonhomogeneous Weather Clutter

The performance of conventional constant false alarm rate (CFAR) detectors may degrade in nonhomogeneous clutter environments, as accurately estimating the clutter distribution in the cell under test (CUT) using reference cells becomes challenging. In this article, a CFAR detector based on clutter s...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on aerospace and electronic systems Ročník 61; číslo 2; s. 3306 - 3322
Hlavní autori:	Yan, Yujia, Hu, Cheng, Cai, Jiong, Li, Weidong, Yu, Teng, Wang, Rui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.04.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Aerospace and electronic systems Algorithms Clutter Clutter segmentation Constant false alarm rate Constraints Detectors False alarms Fields (mathematics) Hidden Markov models hidden Markov random field (HMRF) Meteorology Mixture models Parameter estimation Phased arrays Probabilistic models Radar arrays Radar clutter Radar detection Rain Segmentation Simulation Target detection variational expectation-maximization algorithm Weather weather clutter
ISSN:	0018-9251, 1557-9603
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	The performance of conventional constant false alarm rate (CFAR) detectors may degrade in nonhomogeneous clutter environments, as accurately estimating the clutter distribution in the cell under test (CUT) using reference cells becomes challenging. In this article, a CFAR detector based on clutter segmentation with spatial continuity constraints is proposed for target detection within nonhomogeneous weather clutter backgrounds. Analysis of real weather clutter collected by a high-resolution phased array radar indicates that the Rayleigh mixture model can precisely characterize the amplitude distribution of nonhomogeneous weather clutter in spatial domain. The hidden Markov random field model is employed to capture the spatial correlation of weather clutter. Based on this model, clutter segmentation is implemented using the variational expectation-maximization algorithm, which provides the posterior class of clutter in each range cell and the estimated parameter of each class. Simulation results indicate that introducing the spatial continuity improves the accuracy of clutter segmentation and parameter estimation. A CFAR detection scheme is proposed, which utilizes the segmentation results to estimate the clutter distribution of the CUT and set the detection threshold accordingly. Experiments conducted using both simulated data and real weather clutter have demonstrated that the proposed method improves detection performance. The proposed method exhibit a maximum increase in detection probability of 8.97% compared to the best-performing benchmark method when the false alarm rate is 10^{-6} in real weather clutter.
AbstractList	The performance of conventional constant false alarm rate (CFAR) detectors may degrade in nonhomogeneous clutter environments, as accurately estimating the clutter distribution in the cell under test (CUT) using reference cells becomes challenging. In this article, a CFAR detector based on clutter segmentation with spatial continuity constraints is proposed for target detection within nonhomogeneous weather clutter backgrounds. Analysis of real weather clutter collected by a high-resolution phased array radar indicates that the Rayleigh mixture model can precisely characterize the amplitude distribution of nonhomogeneous weather clutter in spatial domain. The hidden Markov random field model is employed to capture the spatial correlation of weather clutter. Based on this model, clutter segmentation is implemented using the variational expectation-maximization algorithm, which provides the posterior class of clutter in each range cell and the estimated parameter of each class. Simulation results indicate that introducing the spatial continuity improves the accuracy of clutter segmentation and parameter estimation. A CFAR detection scheme is proposed, which utilizes the segmentation results to estimate the clutter distribution of the CUT and set the detection threshold accordingly. Experiments conducted using both simulated data and real weather clutter have demonstrated that the proposed method improves detection performance. The proposed method exhibit a maximum increase in detection probability of 8.97% compared to the best-performing benchmark method when the false alarm rate is 10^{-6} in real weather clutter.
Author	Cai, Jiong Yu, Teng Li, Weidong Yan, Yujia Hu, Cheng Wang, Rui
Author_xml	– sequence: 1 givenname: Yujia orcidid: 0000-0003-0759-8722 surname: Yan fullname: Yan, Yujia organization: Radar Research Laboratory, School of Information and Electronics, Beijing Institute of Technology, Beijing, China – sequence: 2 givenname: Cheng orcidid: 0000-0001-7582-5291 surname: Hu fullname: Hu, Cheng organization: Radar Research Laboratory, School of Information and Electronics, Beijing Institute of Technology, Beijing, China – sequence: 3 givenname: Jiong orcidid: 0000-0003-3911-5510 surname: Cai fullname: Cai, Jiong email: az123jiong@foxmail.com organization: Radar Research Laboratory, School of Information and Electronics, Beijing Institute of Technology, Beijing, China – sequence: 4 givenname: Weidong orcidid: 0000-0002-1774-7800 surname: Li fullname: Li, Weidong organization: Radar Research Laboratory, School of Information and Electronics, Beijing Institute of Technology, Beijing, China – sequence: 5 givenname: Teng surname: Yu fullname: Yu, Teng organization: Radar Research Laboratory, School of Information and Electronics, Beijing Institute of Technology, Beijing, China – sequence: 6 givenname: Rui orcidid: 0000-0002-3510-7356 surname: Wang fullname: Wang, Rui organization: Radar Research Laboratory, School of Information and Electronics, Beijing Institute of Technology, Beijing, China
BookMark	eNpNkF9PwjAUxRujiYB-ABMfmvg8bNdu6x7nBDUhmgiGx6aMWyiBFtvugW_vFnjwvtw_Oeee5DdE19ZZQOiBkjGlpHxeVJP5OCUpHzMuCsrEFRrQLCuSMifsGg0IoSIp04zeomEIu27lgrMB2lR4DpsD2KiicTZ5UQHWuJ5W3_gVIjTRebw0cYvnx06g9rh2NhrbmnjqxxC9MjZiY_Gns1t3cBuw4NqAl6DiFjyu922M4O_QjVb7APeXPkI_08mifk9mX28fdTVLGkZFTDKdKiLybEWJpopzzTLNuRJ5uWaNalZNKXSuhFbFWjcpLcpUZDlb6-5CgQjBRujp_Pfo3W8LIcqda73tImUX0BVned6p6FnVeBeCBy2P3hyUP0lKZM9T9jxlz1NeeHaex7PHAMA_fcGKlFH2B8XIdB4
CODEN	IEARAX
Cites_doi	10.1109/TAES.2023.3245053 10.1109/78.157297 10.1109/TAES.1976.308352 10.1109/TAES.2023.3286830 10.1109/TPAMI.2003.1240112 10.1109/TGRS.2021.3131272 10.1109/7.53424 10.1109/TGRS.2023.3275163 10.1109/TAES.2017.2671798 10.1109/TGRS.2014.2349575 10.1109/78.887045 10.1109/MSP.2004.1311138 10.1109/TIP.2006.891045 10.1109/TMI.2006.880682 10.1109/TGRS.2022.3205737 10.1007/s11432-023-3889-7 10.1109/TGRS.2015.2506822 10.1155/2022/7596449 10.1109/79.543975 10.1109/TAES.2022.3206256 10.1109/TSP.2021.3050985 10.1109/TSP.2023.3250084 10.1126/science.aat7526 10.1109/TAES.2014.130209 10.1109/TAES.1978.308554 10.1109/TIP.2010.2047903 10.1109/MSP.2008.929620 10.1109/TAES.2020.3046050 10.1109/TAES.2020.3017319 10.1109/83.210863 10.1109/7.7185 10.1080/17455030601097927 10.1109/42.906424 10.1080/01621459.1951.10500769 10.1109/TGRS.2023.3250990 10.1007/s11432-020-3211-8 10.1109/7.766939 10.1109/TGRS.2015.2451311 10.1109/TAES.2017.2740065 10.1109/TAES.1984.310453
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
DBID	97E RIA RIE AAYXX CITATION 7SP 7TB 8FD FR3 H8D L7M
DOI	10.1109/TAES.2024.3487138
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database Aerospace Database Advanced Technologies Database with Aerospace
DatabaseTitle	CrossRef Aerospace Database Engineering Research Database Technology Research Database Mechanical & Transportation Engineering Abstracts Advanced Technologies Database with Aerospace Electronics & Communications Abstracts
DatabaseTitleList	Aerospace Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1557-9603
EndPage	3322
ExternalDocumentID	10_1109_TAES_2024_3487138 10737231
Genre	orig-research
GrantInformation_xml	– fundername: National Defense Basic Scientific Research program of China grantid: JCKY2020602C019 funderid: 10.13039/501100012335
GroupedDBID	-~X 0R~ 29I 4.4 41~ 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACIWK ACNCT AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P H~9 IAAWW IBMZZ ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 OCL P2P RIA RIE RNS TN5 VH1 AAYXX CITATION 7SP 7TB 8FD FR3 H8D L7M
ID	FETCH-LOGICAL-c318t-5f2a0865b10f1a44f35f44a869d3cacbc98f6a8fa7dfc217928563df8fa1e0883
IEDL.DBID	RIE
ISICitedReferencesCount	0
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001464959300047&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0018-9251
IngestDate	Tue Jul 22 18:41:12 EDT 2025 Sat Nov 29 08:02:23 EST 2025 Wed Aug 27 02:03:48 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	2
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c318t-5f2a0865b10f1a44f35f44a869d3cacbc98f6a8fa7dfc217928563df8fa1e0883
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-0759-8722 0000-0002-3510-7356 0000-0002-1774-7800 0000-0001-7582-5291 0000-0003-3911-5510
PQID	3188884366
PQPubID	85477
PageCount	17
ParticipantIDs	crossref_primary_10_1109_TAES_2024_3487138 proquest_journals_3188884366 ieee_primary_10737231
PublicationCentury	2000
PublicationDate	2025-04-01
PublicationDateYYYYMMDD	2025-04-01
PublicationDate_xml	– month: 04 year: 2025 text: 2025-04-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on aerospace and electronic systems
PublicationTitleAbbrev	T-AES
PublicationYear	2025
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref35 ref12 ref34 ref15 ref37 ref14 ref31 ref30 ref11 ref33 ref32 ref2 ref1 ref39 ref16 ref38 ref19 ref18 ref24 ref23 ref26 ref25 ref20 ref42 ref41 ref22 ref44 ref43 Celeux (ref36) 2003; 36 ref28 ref27 Xie (ref10) 2023; 39 ref29 ref8 ref7 ref9 Cheng (ref21) 2022; 128 ref4 Yang (ref17) 2018; 151 ref3 ref6 ref5 ref40
References_xml	– ident: ref9 doi: 10.1109/TAES.2023.3245053 – ident: ref35 doi: 10.1109/78.157297 – ident: ref31 doi: 10.1109/TAES.1976.308352 – ident: ref2 doi: 10.1109/TAES.2023.3286830 – volume: 36 start-page: 131 issue: 1 volume-title: Pattern Recognit. year: 2003 ident: ref36 article-title: EM procedures using mean field-like approximations for Markov model-based image segmentation – ident: ref18 doi: 10.1109/TPAMI.2003.1240112 – ident: ref24 doi: 10.1109/TGRS.2021.3131272 – ident: ref40 doi: 10.1109/7.53424 – ident: ref5 doi: 10.1109/TGRS.2023.3275163 – ident: ref42 doi: 10.1109/TAES.2017.2671798 – volume: 151 start-page: 144 volume-title: Signal Process. year: 2018 ident: ref17 article-title: MRF model-based joint interrupted SAR imaging and coherent change detection via variational bayesian inference – ident: ref23 doi: 10.1109/TGRS.2014.2349575 – ident: ref28 doi: 10.1109/78.887045 – ident: ref37 doi: 10.1109/MSP.2004.1311138 – ident: ref33 doi: 10.1109/TIP.2006.891045 – ident: ref16 doi: 10.1109/TMI.2006.880682 – ident: ref4 doi: 10.1109/TGRS.2022.3205737 – ident: ref25 doi: 10.1007/s11432-023-3889-7 – ident: ref13 doi: 10.1109/TGRS.2015.2506822 – ident: ref30 doi: 10.1155/2022/7596449 – ident: ref29 doi: 10.1109/79.543975 – ident: ref8 doi: 10.1109/TAES.2022.3206256 – ident: ref15 doi: 10.1109/TSP.2021.3050985 – ident: ref6 doi: 10.1109/TSP.2023.3250084 – ident: ref1 doi: 10.1126/science.aat7526 – ident: ref14 doi: 10.1109/TAES.2014.130209 – ident: ref38 doi: 10.1109/TAES.1978.308554 – ident: ref20 doi: 10.1109/TIP.2010.2047903 – ident: ref34 doi: 10.1109/MSP.2008.929620 – ident: ref44 doi: 10.1109/TAES.2020.3046050 – ident: ref7 doi: 10.1109/TAES.2020.3017319 – volume: 39 start-page: 496 issue: 3 year: 2023 ident: ref10 article-title: Ship target detection method in SAR imagery based on eigenvalue decomposition of the toeplitz matrix publication-title: J. Signal Process. – ident: ref19 doi: 10.1109/83.210863 – ident: ref43 doi: 10.1109/7.7185 – ident: ref32 doi: 10.1080/17455030601097927 – ident: ref22 doi: 10.1109/42.906424 – volume: 128 volume-title: Pattern Recognit. year: 2022 ident: ref21 article-title: A spatially constrained skew Student’st mixture model for brain MR image segmentation and bias field correction – ident: ref27 doi: 10.1080/01621459.1951.10500769 – ident: ref3 doi: 10.1109/TGRS.2023.3250990 – ident: ref11 doi: 10.1007/s11432-020-3211-8 – ident: ref26 doi: 10.1109/7.766939 – ident: ref41 doi: 10.1109/TGRS.2015.2451311 – ident: ref12 doi: 10.1109/TAES.2017.2740065 – ident: ref39 doi: 10.1109/TAES.1984.310453
SSID	ssj0014843
Score	2.4520876
Snippet	The performance of conventional constant false alarm rate (CFAR) detectors may degrade in nonhomogeneous clutter environments, as accurately estimating the...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Index Database Publisher
StartPage	3306
SubjectTerms	Aerospace and electronic systems Algorithms Clutter Clutter segmentation Constant false alarm rate Constraints Detectors False alarms Fields (mathematics) Hidden Markov models hidden Markov random field (HMRF) Meteorology Mixture models Parameter estimation Phased arrays Probabilistic models Radar arrays Radar clutter Radar detection Rain Segmentation Simulation Target detection variational expectation-maximization algorithm Weather weather clutter
Title	A Segmentation-Based CFAR Detector With Spatial Continuity Constraint in Nonhomogeneous Weather Clutter
URI	https://ieeexplore.ieee.org/document/10737231 https://www.proquest.com/docview/3188884366
Volume	61
WOSCitedRecordID	wos001464959300047&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1557-9603 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014843 issn: 0018-9251 databaseCode: RIE dateStart: 19650101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NT4MwFG908aAHP2ecTtODJxMm0FLKEecWT4txM9uNQGk3EgdmgH-_r4WZJcaDnFoCCfR9t--9H0L3hOg-SpJYgRS2RVOIWSEEkzBlrnA9lyexMmAT_mTCF4vgtS1WN7UwUkqTfCYHemjO8tNC1HqrDCRcg6roqul932dNsdbPkQHlbYqcAxIMVrs9wnTs4HEWjqYQCrp0QMA_d3Qtyo4RMqgqv1SxsS_jk39-2Sk6bh1JHDaUP0N7Mj9HRzvtBS_QMsRTuVy31UW59QQWK8XDcfiGn2VltuvxPKtWWOMSAx9i3aoqy2twzPWwNPARFc5yPCnyVbEugNlkUZd43viNePhhYK676H08mg1frBZYwRIgwpXlKTeGUMZLHFs5MaWKeIrSmLMgJSIWiQi4YjFXsZ8qATFL4HKPkVTBHUeCWiKXqJMXubxC2E4TmLMkUUx3E3MTD1RETLnnOyplCe-hh-1KR59N_4zIxB12EGmyRJosUUuWHurqpd15sFnVHupviRO1IlZG8CdwUcLY9R-v3aBDV6P1mjybPupUm1reogPxVWXl5s5wzzeEPsJy
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NT4MwFG-MmqgHv43zswdPJii0pSvHOV00TmLczHYjUNqNxIER5t_va0GzxHiQU0sggb7v9r33Q-iCUtNHSVEnUNJ1WAoxK4RgCqacSOITkcTagk20w1CMx8FzU6xua2GUUjb5TF2ZoT3LTws5N1tlIOEGVMVUTa_4jBG3Ltf6OTRgokmS80CGwW43h5ieG1wPO3cDCAYJu6LgoXumGmXBDFlclV_K2FqY3tY_v20bbTauJO7UtN9BSyrfRRsLDQb30KSDB2oya-qLcucGbFaKu73OC75Vld2wx6OsmmKDTAyciE2zqiyfg2tuhqUFkKhwluOwyKfFrAB2U8W8xKPac8TdNwt0vY9ee3fD7r3TQCs4EoS4cnxNYghm_MRztRczpqmvGYsFD1IqY5nIQGgeCx23Uy0hagmI8DlNNdzxFCgmeoCW8yJXhwi7aQJzniSam35iJPFBScRM-G1PpzwRLXT5vdLRe91BI7KRhxtEhiyRIUvUkKWF9s3SLjxYr2oLnXwTJ2qErIzgT-BilPOjP147R2v3w6d-1H8IH4_ROjHYvTbr5gQtVx9zdYpW5WeVlR9nlpO-AMyIxbk
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=A+Segmentation-Based+CFAR+Detector+With+Spatial+Continuity+Constraint+in+Nonhomogeneous+Weather+Clutter&rft.jtitle=IEEE+transactions+on+aerospace+and+electronic+systems&rft.au=Yan%2C+Yujia&rft.au=Hu%2C+Cheng&rft.au=Cai%2C+Jiong&rft.au=Li%2C+Weidong&rft.date=2025-04-01&rft.pub=IEEE&rft.issn=0018-9251&rft.volume=61&rft.issue=2&rft.spage=3306&rft.epage=3322&rft_id=info:doi/10.1109%2FTAES.2024.3487138&rft.externalDocID=10737231
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9251&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9251&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9251&client=summon