{"id":"https://openalex.org/W7147017510","doi":"https://doi.org/10.1109/cnml68938.2026.11453018","title":"A Deep Learning-Based Method for Separating Overlapping ADS-B Signals in Continuous Single-Antenna Reception","display_name":"A Deep Learning-Based Method for Separating Overlapping ADS-B Signals in Continuous Single-Antenna Reception","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7147017510","doi":"https://doi.org/10.1109/cnml68938.2026.11453018"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11453018","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11453018","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-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/A5100363970","display_name":"Chenchen Wang","orcid":"https://orcid.org/0000-0001-9058-6428"},"institutions":[{"id":"https://openalex.org/I28813325","display_name":"Civil Aviation University of China","ror":"https://ror.org/03je71k37","country_code":"CN","type":"education","lineage":["https://openalex.org/I28813325"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chenchen Wang","raw_affiliation_strings":["Civil Aviation University of China,Tianjin Key Lab for Advanced Signal Processing,Tianjin,China"],"affiliations":[{"raw_affiliation_string":"Civil Aviation University of China,Tianjin Key Lab for Advanced Signal Processing,Tianjin,China","institution_ids":["https://openalex.org/I28813325"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126702011","display_name":"Wenyi Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I28813325","display_name":"Civil Aviation University of China","ror":"https://ror.org/03je71k37","country_code":"CN","type":"education","lineage":["https://openalex.org/I28813325"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenyi Wang","raw_affiliation_strings":["Civil Aviation University of China,Tianjin Key Lab for Advanced Signal Processing,Tianjin,China"],"affiliations":[{"raw_affiliation_string":"Civil Aviation University of China,Tianjin Key Lab for Advanced Signal Processing,Tianjin,China","institution_ids":["https://openalex.org/I28813325"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100363970"],"corresponding_institution_ids":["https://openalex.org/I28813325"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.88286701,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"434","last_page":"437"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.6647999882698059,"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":0.6647999882698059,"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/T10579","display_name":"Cognitive Radio Networks and Spectrum Sensing","score":0.04859999939799309,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.030400000512599945,"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/redundancy","display_name":"Redundancy (engineering)","score":0.5364999771118164},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4242999851703644},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.40700000524520874},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.39329999685287476},{"id":"https://openalex.org/keywords/blind-signal-separation","display_name":"Blind signal separation","score":0.3668999969959259},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35569998621940613},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.34209999442100525},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.31290000677108765}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5705000162124634},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5364999771118164},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4242999851703644},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.40700000524520874},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4036000072956085},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4032000005245209},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.39329999685287476},{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.3668999969959259},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35569998621940613},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3447999954223633},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.31290000677108765},{"id":"https://openalex.org/C2776902269","wikidata":"https://www.wikidata.org/wiki/Q5165493","display_name":"Continuous monitoring","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C2776864781","wikidata":"https://www.wikidata.org/wiki/Q52617913","display_name":"Source separation","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11453018","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11453018","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2015104437","https://openalex.org/W2097609833","https://openalex.org/W2129391519","https://openalex.org/W2460742184","https://openalex.org/W2964058413","https://openalex.org/W3015199127","https://openalex.org/W3163652268","https://openalex.org/W3164210919","https://openalex.org/W4294132113","https://openalex.org/W4308347800"],"related_works":[],"abstract_inverted_index":{"Automatic":[0],"Dependent":[1],"Surveillance-Broadcast":[2],"(ADS-B)":[3],"signals":[4,37,75,103,151,197],"are":[5,20,43],"prone":[6],"to":[7,12,71,82],"in-air":[8],"overlapping,":[9],"which":[10,28],"leads":[11],"decoding":[13],"failures.":[14],"Most":[15],"existing":[16],"deep":[17],"learning-based":[18],"methods":[19],"trained":[21],"and":[22,47,107,172,180],"evaluated":[23],"on":[24,132,147,174],"pre-segmented":[25],"overlapping":[26,95],"signals,":[27],"differs":[29],"from":[30],"real":[31],"reception":[32],"scenarios.":[33],"In":[34],"practice,":[35],"continuous":[36,73,105,149,196],"received":[38,198],"by":[39,45,170,199],"a":[40,51,118,200],"single":[41,201],"antenna":[42],"dominated":[44],"noise":[46],"interference,":[48],"with":[49,155],"only":[50],"few":[52],"randomly":[53],"occurring":[54],"ADS-B":[55,65],"signal":[56,66,134],"segments.":[57],"To":[58],"address":[59],"this":[60,89],"issue,":[61],"an":[62],"end-to-end":[63],"time-domain":[64],"separation":[67],"method":[68,162,194],"is":[69,79,124],"proposed":[70,127,161,193],"process":[72],"observation":[74,150],"directly.":[76],"This":[77],"work":[78],"the":[80,84,98,108,130,140,156,160,189,192],"first":[81],"introduce":[83],"overlap-add":[85],"(OLA)":[86],"strategy":[87],"into":[88],"task,":[90],"enabling":[91],"direct":[92],"recovery":[93],"of":[94,101,111,142,191],"signals.":[96],"Considering":[97],"low":[99],"proportion":[100],"effective":[102,133],"in":[104],"data":[106],"insufficient":[109],"constraints":[110],"conventional":[112],"scale-invariant":[113],"source-to-noise":[114],"ratio":[115],"(SI-SNR)":[116],"loss,":[117,159],"log-envelope":[119],"region-weighted":[120],"SI-SNR":[121,158],"(LERW-SNR)":[122],"loss":[123,128],"designed.":[125],"The":[126],"enhances":[129],"emphasis":[131],"regions":[135],"during":[136],"training":[137],"while":[138],"suppressing":[139],"influence":[141],"background":[143],"noise.":[144],"Experimental":[145],"results":[146,187],"real-world":[148],"show":[152],"that,":[153],"compared":[154],"standard":[157],"improves":[163],"cyclic":[164],"redundancy":[165],"check":[166],"(CRC)":[167],"pass":[168],"rates":[169],"3.27%":[171],"11.24%":[173],"Dual-Path":[175],"Recurrent":[176],"Neural":[177],"Network":[178],"(DPRNN)":[179],"Separation":[181],"Transformer":[182],"(Sepformer)":[183],"networks,":[184],"respectively.":[185],"These":[186],"demonstrate":[188],"effectiveness":[190],"for":[195],"antenna.":[202]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-04-02T00:00:00"}
