{"id":"https://openalex.org/W7147630185","doi":"https://doi.org/10.48550/arxiv.2603.29187","title":"Needle in a Haystack: Tracking UAVs from Massive Noise in Real-World 5G-A Base Station Data","display_name":"Needle in a Haystack: Tracking UAVs from Massive Noise in Real-World 5G-A Base Station Data","publication_year":2026,"publication_date":"2026-03-31","ids":{"openalex":"https://openalex.org/W7147630185","doi":"https://doi.org/10.48550/arxiv.2603.29187"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.29187","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29187","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.29187","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023863179","display_name":"Chengzhen Meng","orcid":"https://orcid.org/0009-0006-4285-1905"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng, Chengzhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132566008","display_name":"Chenming He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Chenming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109778698","display_name":"Yidong Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Yidong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101588749","display_name":"Xiaoran Fan","orcid":"https://orcid.org/0000-0002-6368-9250"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Xiaoran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101424814","display_name":"Dequan Wang","orcid":"https://orcid.org/0000-0003-0877-4636"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Dequan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132679713","display_name":"Lingyu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Lingyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132711142","display_name":"Jianmin Ji","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ji, Jianmin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132592234","display_name":"Yanyong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yanyong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11133","display_name":"UAV Applications and Optimization","score":0.878000020980835,"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"}},"topics":[{"id":"https://openalex.org/T11133","display_name":"UAV Applications and Optimization","score":0.878000020980835,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.04859999939799309,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.01489999983459711,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6596999764442444},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6417999863624573},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6025000214576721},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5259000062942505},{"id":"https://openalex.org/keywords/base-station","display_name":"Base station","score":0.5097000002861023},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4708000123500824},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4620000123977661},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4449000060558319},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.4377000033855438},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.41339999437332153}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7325000166893005},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6596999764442444},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6417999863624573},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6025000214576721},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.5554999709129333},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5259000062942505},{"id":"https://openalex.org/C68649174","wikidata":"https://www.wikidata.org/wiki/Q1379116","display_name":"Base station","level":2,"score":0.5097000002861023},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49230000376701355},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4708000123500824},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4620000123977661},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4449000060558319},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.4377000033855438},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.427700012922287},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.41339999437332153},{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.4092999994754791},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.40380001068115234},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4002000093460083},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.390500009059906},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.37549999356269836},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C154586513","wikidata":"https://www.wikidata.org/wiki/Q4420972","display_name":"Tracking system","level":3,"score":0.3653999865055084},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.34279999136924744},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3343000113964081},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.32690000534057617},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2906000018119812},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.2867000102996826},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.29187","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29187","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.29187","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29187","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,57,211],"potential":[1],"usage":[2],"of":[3,65,230,245,251,262,269],"UAVs":[4,18,47,83],"in":[5,61,75,94,207],"daily":[6],"life":[7],"has":[8],"made":[9],"monitoring":[10,17],"them":[11],"essential.":[12],"However,":[13],"existing":[14],"systems":[15],"for":[16,171],"typically":[19],"rely":[20],"on":[21,200],"cameras,":[22],"LiDARs,":[23],"or":[24,30],"radars,":[25],"whose":[26],"limited":[27],"sensing":[28],"range":[29],"high":[31],"deployment":[32],"cost":[33],"hinder":[34],"large-scale":[35],"adoption.":[36],"In":[37],"response,":[38],"we":[39,100,120,136,148,180],"develop":[40],"BSense,":[41],"the":[42,62,85,109,116,145,176,192,243],"first":[43],"system":[44,241],"that":[45,68,105,122,185],"tracks":[46],"by":[48,164],"leveraging":[49],"point":[50,87,118],"clouds":[51,88],"from":[52,84,125,248],"commercial":[53,202],"5G-A":[54,203],"base":[55,204],"stations.":[56],"key":[58],"challenge":[59],"lies":[60],"dominant":[63],"number":[64,244],"noise":[66,107,123],"points":[67,124],"closely":[69],"resemble":[70],"true":[71],"UAV":[72,212],"points,":[73],"resulting":[74],"a":[76,92,95,102,182,201,265],"noise-to-UAV":[77],"ratio":[78],"over":[79,168,224],"100:1.":[80],"Therefore,":[81],"identifying":[82],"raw":[86,117],"is":[89],"like":[90],"finding":[91],"needle":[93],"haystack.":[96],"To":[97],"overcome":[98],"this,":[99],"propose":[101,181],"layered":[103],"framework":[104],"filters":[106],"at":[108,272],"point,":[110],"object,":[111],"and":[112,131,141,151,159,264],"trajectory":[113,178],"levels.":[114],"At":[115,144,175],"level,":[119,147,179],"observe":[121],"different":[126],"spatial":[127,150],"regions":[128],"exhibit":[129],"distinguishable":[130],"consistent":[132],"signal":[133],"fingerprints,":[134],"which":[135],"can":[137],"model":[138],"to":[139,155,190,215,255,275],"identify":[140,156],"remove":[142],"them.":[143],"object":[146],"design":[149],"velocity":[152],"consistency":[153],"checks":[154,167],"false":[157,195,246],"objects,":[158],"further":[160],"compute":[161],"confidence":[162],"scores":[163],"aggregating":[165],"these":[166],"multiple":[169],"frames":[170],"more":[172,233],"reliable":[173],"discrimination.":[174],"final":[177],"Transformer-based":[183],"network":[184],"captures":[186],"multi-frame":[187],"motion":[188],"patterns":[189],"filter":[191],"few":[193],"remaining":[194],"trajectories.":[196],"We":[197],"evaluated":[198],"BSense":[199],"station":[205],"deployed":[206],"an":[208,249,258],"urban":[209],"environment.":[210],"was":[213],"instructed":[214],"fly":[216],"along":[217],"25":[218],"distinct":[219],"trajectories":[220],"across":[221],"54":[222],"cases":[223],"7":[225],"days,":[226],"yielding":[227],"155":[228],"minutes":[229],"data":[231],"with":[232],"than":[234],"14,000":[235],"frames.":[236],"On":[237],"this":[238],"dataset,":[239],"our":[240],"reduces":[242],"detections":[247],"average":[250,259],"168.05":[252],"per":[253],"frame":[254],"0.04,":[256],"achieving":[257],"F1":[260],"score":[261],"95.56%":[263],"mean":[266],"localization":[267],"error":[268],"4.9":[270],"m":[271],"ranges":[273],"up":[274],"1,000":[276],"m.":[277]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-02T00:00:00"}