{"id":"https://openalex.org/W7129681320","doi":"https://doi.org/10.48550/arxiv.2602.13296","title":"MFN Decomposition and Related Metrics for High-Resolution Range Profiles Generative Models","display_name":"MFN Decomposition and Related Metrics for High-Resolution Range Profiles Generative Models","publication_year":2026,"publication_date":"2026-02-09","ids":{"openalex":"https://openalex.org/W7129681320","doi":"https://doi.org/10.48550/arxiv.2602.13296"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.13296","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.13296","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.2602.13296","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":0.7591000199317932,"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":0.7591000199317932,"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/T11448","display_name":"Face recognition and analysis","score":0.11230000108480453,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0203000009059906,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7491999864578247},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5550000071525574},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.503000020980835},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4788999855518341},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45179998874664307},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4203999936580658},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.38119998574256897}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7491999864578247},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6579999923706055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6171000003814697},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5550000071525574},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.503000020980835},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4788999855518341},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4754999876022339},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45179998874664307},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4275999963283539},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4203999936580658},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.38119998574256897},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.3804999887943268},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3621000051498413},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3183000087738037},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.31779998540878296},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.27300000190734863}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.13296","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.13296","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.2602.13296","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.13296","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":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7709757089614868}],"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],"profile":[2],"(HRRP":[3],")":[4],"data":[5,37,45,75,85],"are":[6],"in":[7,9,18,25,54],"vogue":[8],"radar":[10],"automatic":[11],"target":[12],"recognition":[13],"(RATR).":[14],"With":[15],"the":[16,50,55,89,91,94,105,128],"interest":[17],"classifying":[19],"models":[20,29],"using":[21,27],"HRRP,":[22],"filling":[23],"gaps":[24],"datasets":[26],"generative":[28],"has":[30],"recently":[31],"received":[32],"promising":[33],"contributions.":[34],"Evaluating":[35],"generated":[36,74],"is":[38],"a":[39,123],"challenging":[40,124],"topic,":[41],"even":[42],"for":[43],"explicit":[44],"like":[46],"face":[47],"images.":[48],"However,":[49],"evaluation":[51],"methods":[52],"used":[53],"state-ofthe-art":[56],"of":[57,108,131],"HRRP":[58,84],"generation":[59],"rely":[60],"on":[61,73,82,104,122],"classification":[62],"models.":[63],"Such":[64],"models,":[65],"called":[66],"''black-box'',":[67],"do":[68],"not":[69],"allow":[70],"either":[71],"explainability":[72],"or":[76],"multi-level":[77],"evaluation.":[78],"This":[79],"work":[80],"focuses":[81],"decomposing":[83],"into":[86],"three":[87],"components:":[88],"mask,":[90],"features,":[92],"and":[93,126],"noise.":[95],"Using":[96],"this":[97],"decomposition,":[98],"we":[99],"propose":[100],"two":[101],"metrics":[102,121],"based":[103],"physical":[106],"interpretation":[107],"those":[109],"data.":[110],"We":[111],"take":[112],"profit":[113],"from":[114],"an":[115],"expensive":[116],"dataset":[117],"to":[118],"evaluate":[119],"our":[120],"task":[125],"demonstrate":[127],"discriminative":[129],"ability":[130],"those.":[132]},"counts_by_year":[],"updated_date":"2026-02-18T06:25:47.457606","created_date":"2026-02-18T00:00:00"}