{"id":"https://openalex.org/W7147309645","doi":"https://doi.org/10.1109/taes.2026.3679318","title":"A Unified Antijamming Design in Complex Environments Based on Cross-Modal Fusion and Intelligent Decision-Making","display_name":"A Unified Antijamming Design in Complex Environments Based on Cross-Modal Fusion and Intelligent Decision-Making","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7147309645","doi":"https://doi.org/10.1109/taes.2026.3679318"},"language":null,"primary_location":{"id":"doi:10.1109/taes.2026.3679318","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taes.2026.3679318","pdf_url":null,"source":{"id":"https://openalex.org/S193624734","display_name":"IEEE Transactions on Aerospace and Electronic Systems","issn_l":"0018-9251","issn":["0018-9251","1557-9603","2371-9877"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Aerospace and Electronic Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101939167","display_name":"Huake Wang","orcid":"https://orcid.org/0000-0002-0905-8108"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huake Wang","raw_affiliation_strings":["Hangzhou Institute of Technology, Xidian University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-0905-8108","affiliations":[{"raw_affiliation_string":"Hangzhou Institute of Technology, Xidian University, Hangzhou, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123400714","display_name":"Xudong Han","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xudong Han","raw_affiliation_strings":["Hangzhou Institute of Technology, Xidian University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0009-4430-8289","affiliations":[{"raw_affiliation_string":"Hangzhou Institute of Technology, Xidian University, Hangzhou, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132596635","display_name":"Bairui Cai","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bairui Cai","raw_affiliation_strings":["Hangzhou Institute of Technology, Xidian University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Institute of Technology, Xidian University, Hangzhou, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132679431","display_name":"Guisheng Liao","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guisheng Liao","raw_affiliation_strings":["Hangzhou Institute of Technology, Xidian University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-5919-0713","affiliations":[{"raw_affiliation_string":"Hangzhou Institute of Technology, Xidian University, Hangzhou, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5121492524","display_name":"Yinghui Quan","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinghui Quan","raw_affiliation_strings":["Hangzhou Institute of Technology, Xidian University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-6541-9441","affiliations":[{"raw_affiliation_string":"Hangzhou Institute of Technology, Xidian University, Hangzhou, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36119541,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"62","issue":null,"first_page":"9119","last_page":"9131"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.42890000343322754,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.42890000343322754,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10891","display_name":"Radar Systems and Signal Processing","score":0.34769999980926514,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.14790000021457672,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/jamming","display_name":"Jamming","score":0.7110000252723694},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.6708999872207642},{"id":"https://openalex.org/keywords/automatic-target-recognition","display_name":"Automatic target recognition","score":0.6514000296592712},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.5580999851226807},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4884999990463257},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4875999987125397},{"id":"https://openalex.org/keywords/radar-cross-section","display_name":"Radar cross-section","score":0.39399999380111694},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.3905999958515167},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3873000144958496},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3734999895095825}],"concepts":[{"id":"https://openalex.org/C2779079576","wikidata":"https://www.wikidata.org/wiki/Q17092823","display_name":"Jamming","level":2,"score":0.7110000252723694},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.6708999872207642},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6610999703407288},{"id":"https://openalex.org/C117623542","wikidata":"https://www.wikidata.org/wiki/Q621974","display_name":"Automatic target recognition","level":3,"score":0.6514000296592712},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5849000215530396},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.5580999851226807},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4884999990463257},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4875999987125397},{"id":"https://openalex.org/C101457746","wikidata":"https://www.wikidata.org/wiki/Q560430","display_name":"Radar cross-section","level":3,"score":0.39399999380111694},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3905999958515167},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3873000144958496},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3734999895095825},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.3569999933242798},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3449999988079071},{"id":"https://openalex.org/C176381164","wikidata":"https://www.wikidata.org/wiki/Q1486507","display_name":"Radar jamming and deception","level":5,"score":0.33399999141693115},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.3321000039577484},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.3068999946117401},{"id":"https://openalex.org/C30406889","wikidata":"https://www.wikidata.org/wiki/Q3707600","display_name":"Digital radio frequency memory","level":5,"score":0.2996000051498413},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.2937000095844269},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C184892835","wikidata":"https://www.wikidata.org/wiki/Q1474513","display_name":"Electromagnetic interference","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C64183698","wikidata":"https://www.wikidata.org/wiki/Q4530886","display_name":"Electromagnetic environment","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2639000117778778},{"id":"https://openalex.org/C176563091","wikidata":"https://www.wikidata.org/wiki/Q669238","display_name":"Intelligent sensor","level":3,"score":0.26269999146461487},{"id":"https://openalex.org/C2779726219","wikidata":"https://www.wikidata.org/wiki/Q7685884","display_name":"Target acquisition","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C75172450","wikidata":"https://www.wikidata.org/wiki/Q623950","display_name":"Fast Fourier transform","level":2,"score":0.25119999051094055},{"id":"https://openalex.org/C147345108","wikidata":"https://www.wikidata.org/wiki/Q6693040","display_name":"Low probability of intercept radar","level":5,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taes.2026.3679318","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taes.2026.3679318","pdf_url":null,"source":{"id":"https://openalex.org/S193624734","display_name":"IEEE Transactions on Aerospace and Electronic Systems","issn_l":"0018-9251","issn":["0018-9251","1557-9603","2371-9877"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Aerospace and Electronic Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.721203088760376,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G1761169979","display_name":null,"funder_award_id":"62301410","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G620763321","display_name":null,"funder_award_id":"62331019","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,14,23,29,130,152,157,166,191,201,224,238,273,276],"rapid":[2],"development":[3],"of":[4,25,31,84,115,129,203,226,257],"radar":[5,32,85,131,193],"jamming":[6,57,182,187,210,219],"systems,":[7],"especially":[8],"digital":[9],"radio":[10],"frequency":[11],"memory":[12],"(DRFM),":[13],"electromagnetic":[15],"environment":[16],"is":[17,106,148],"increasingly":[18],"complicated.":[19],"In":[20,63],"recent":[21],"years,":[22],"application":[24],"neural":[26,46],"networks":[27,47],"in":[28,160,217,244,262,270],"fields":[30],"interference":[33,73,154],"recognition":[34,58,74,158,221],"and":[35,96,223,260,283],"anti-jamming":[36,60,77,177,227],"has":[37],"proven":[38],"to":[39,109,150,180,208,246],"be":[40],"highly":[41],"effective,":[42],"such":[43],"as":[44],"convolutional":[45],"(CNNs).":[48],"However,":[49],"most":[50],"existing":[51],"studies":[52],"solely":[53],"focus":[54],"on":[55,139],"either":[56],"or":[59],"strategy":[61,78,178,228,286],"design.":[62],"this":[64],"paper,":[65],"we":[66],"propose":[67],"a":[68,134,144,172],"unified":[69],"framework":[70],"that":[71,237],"integrates":[72],"with":[75,125,162,254,272],"intelligent":[76,176,198],"selection.":[79],"Specifically,":[80],"time-frequency":[81,116],"(TF)":[82],"features":[83,121,128],"echoes":[86,132],"are":[87,122,169],"first":[88],"extracted":[89],"using":[90],"both":[91,218],"Short-Time":[92],"Fourier":[93],"Transform":[94],"(STFT)":[95],"Smoothed":[97],"Pseudo":[98],"Wigner\u2013Ville":[99],"Distribution":[100],"(SPWVD).":[101],"A":[102],"feature":[103],"fusion":[104,136],"method":[105,214,240],"then":[107],"designed":[108,213,277],"effectively":[110],"combine":[111],"these":[112],"two":[113],"types":[114],"representations.":[117],"The":[118,185,212,233],"fused":[119],"TF":[120],"further":[123],"combined":[124],"time":[126],"domain":[127],"through":[133],"cross-modal":[135],"module":[137],"based":[138],"an":[140],"attention":[141],"mechanism.":[142],"Subsequently,":[143],"three-class":[145],"classification":[146],"algorithm":[147,278],"employed":[149],"identify":[151],"different":[153],"types.":[155,211],"Finally,":[156],"results,":[159],"conjunction":[161],"information":[163,196],"obtained":[164,189],"from":[165],"passive":[167,192],"radar,":[168],"fed":[170],"into":[171],"Deep":[173],"Q-Network":[174],"(DQN)-based":[175],"network":[179],"select":[181],"suppression":[183],"waveforms.":[184],"key":[186],"parameters":[188],"by":[190],"provide":[194],"essential":[195],"for":[197],"decision-making,":[199],"enabling":[200],"generation":[202],"more":[204,284],"effective":[205],"strategies":[206],"tailored":[207],"specific":[209],"demonstrates":[215],"improvements":[216,256],"type":[220],"accuracy":[222,264],"stability":[225],"selection":[229],"under":[230,265],"complex":[231],"environments.":[232],"experimental":[234],"results":[235],"demonstrate":[236],"proposed":[239],"exhibits":[241],"superior":[242],"performance":[243],"comparison":[245,271],"Support":[247],"Vector":[248],"Machines":[249],"(SVM),":[250],"VGG-16,":[251],"2D-CNN":[252],"methods,":[253],"respective":[255],"3.75%,":[258],"1.76%":[259],"2.32%":[261],"overall":[263],"high-accuracy":[266],"operating":[267],"conditions.":[268],"Furthermore,":[269],"SARSA":[274],"algorithm,":[275],"achieves":[279],"faster":[280],"reward":[281],"convergence":[282],"stable":[285],"generation.":[287]},"counts_by_year":[],"updated_date":"2026-05-26T06:15:08.310788","created_date":"2026-04-02T00:00:00"}
