{"id":"https://openalex.org/W7129421865","doi":"https://doi.org/10.48550/arxiv.2602.13297","title":"Conditional Generative Models for High-Resolution Range Profiles: Capturing Geometry-Driven Trends in a Large-Scale Maritime Dataset","display_name":"Conditional Generative Models for High-Resolution Range Profiles: Capturing Geometry-Driven Trends in a Large-Scale Maritime Dataset","publication_year":2026,"publication_date":"2026-02-09","ids":{"openalex":"https://openalex.org/W7129421865","doi":"https://doi.org/10.48550/arxiv.2602.13297"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.13297","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.13297","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.2602.13297","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120152148","display_name":"Edwyn Brient","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Brient, Edwyn","raw_affiliation_strings":["CMM"],"affiliations":[{"raw_affiliation_string":"CMM","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081107954","display_name":"Santiago Velasco-Forero","orcid":"https://orcid.org/0000-0002-2438-1747"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Velasco-Forero, Santiago","raw_affiliation_strings":["CMM"],"affiliations":[{"raw_affiliation_string":"CMM","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074834035","display_name":"Rami Kassab","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kassab, Rami","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5120152148"],"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.8668000102043152,"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.8668000102043152,"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/T11698","display_name":"Underwater Acoustics Research","score":0.054099999368190765,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10891","display_name":"Radar Systems and Signal Processing","score":0.02500000037252903,"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/robustness","display_name":"Robustness (evolution)","score":0.7075999975204468},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5888000130653381},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5131000280380249},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.5126000046730042},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4185999929904938},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.398499995470047}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7075999975204468},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6008999943733215},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5888000130653381},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5182999968528748},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5131000280380249},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.5126000046730042},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4185999929904938},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.398499995470047},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3700999915599823},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34200000762939453},{"id":"https://openalex.org/C2984296375","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar systems","level":3,"score":0.3400000035762787},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.33799999952316284},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.32199999690055847},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C117623542","wikidata":"https://www.wikidata.org/wiki/Q621974","display_name":"Automatic target recognition","level":3,"score":0.2563000023365021}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.13297","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.13297","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.2602.13297","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.13297","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":[{"score":0.8079701662063599,"id":"https://metadata.un.org/sdg/14","display_name":"Life below water"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"High-resolution":[0],"range":[1],"profiles":[2],"(HRRPs)":[3],"enable":[4],"fast":[5],"onboard":[6],"processing":[7],"for":[8,106],"radar":[9],"automatic":[10],"target":[11],"recognition,":[12],"but":[13],"their":[14],"strong":[15],"sensitivity":[16],"to":[17],"acquisition":[18,104],"conditions":[19],"limits":[20],"robustness":[21],"across":[22],"operational":[23],"scenarios.":[24],"Conditional":[25],"HRRP":[26,44,108],"generation":[27],"can":[28],"mitigate":[29],"this":[30],"issue,":[31],"yet":[32],"prior":[33],"studies":[34],"are":[35,64],"constrained":[36],"by":[37],"small,":[38],"highly":[39],"specific":[40],"datasets.":[41],"We":[42],"study":[43],"synthesis":[45],"on":[46,74],"a":[47],"largescale":[48],"maritime":[49],"database":[50],"representative":[51],"of":[52,103],"coastal":[53],"surveillance":[54],"variability.":[55],"Our":[56],"analysis":[57],"indicates":[58],"that":[59,83],"the":[60,69,84,88,100],"fundamental":[61],"scenario":[62],"drivers":[63],"geometric:":[65],"ship":[66],"dimensions":[67],"and":[68,81],"desired":[70],"aspect":[71],"angle.":[72],"Conditioning":[73],"these":[75],"variables,":[76],"we":[77],"train":[78],"generative":[79],"models":[80],"show":[82],"synthesized":[85],"signatures":[86],"reproduce":[87],"expected":[89],"line-of-sight":[90],"geometric":[91],"trend":[92],"observed":[93],"in":[94],"real":[95],"data.":[96],"These":[97],"results":[98],"highlight":[99],"central":[101],"role":[102],"geometry":[105],"robust":[107],"generation.":[109]},"counts_by_year":[],"updated_date":"2026-02-18T06:25:47.457606","created_date":"2026-02-18T00:00:00"}