{"id":"https://openalex.org/W7214066415","doi":"https://doi.org/10.48550/arxiv.2609.25057","title":"Physics-guided deep metric learning with continuous time embeddings for open-world radar pulse de-interleaving","display_name":"Physics-guided deep metric learning with continuous time embeddings for open-world radar pulse de-interleaving","publication_year":2026,"publication_date":"2026-09-08","ids":{"openalex":"https://openalex.org/W7214066415","doi":"https://doi.org/10.48550/arxiv.2609.25057"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.25057","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.25057","pdf_url":"https://arxiv.org/pdf/2609.25057","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2609.25057","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065686582","display_name":"Vikas Agnihotri","orcid":"https://orcid.org/0009-0007-3284-3423","observed_orcids":["https://orcid.org/0009-0007-3284-3423"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agnihotri, Vikas","raw_affiliation_strings":["Independent Researcher India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Independent Researcher India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124852786","display_name":"Jasleen Kaur","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I16292982","display_name":"National Institute of Technology Rourkela","ror":"https://ror.org/011gmn932","country_code":"IN","type":"education","lineage":["https://openalex.org/I16292982"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kaur, Jasleen","raw_affiliation_strings":["National Institute of Technology , Rourkela India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Technology , Rourkela India","institution_ids":["https://openalex.org/I16292982"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10891","display_name":"Radar Systems and Signal Processing","score":0.8866999745368958,"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/T10891","display_name":"Radar Systems and Signal Processing","score":0.8866999745368958,"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":0.06629999727010727,"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.0272000003606081,"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/electronic-support-measures","display_name":"electronic support measures","score":0.8849999904632568},{"id":"https://openalex.org/keywords/open-world-classification","display_name":"open-world classification","score":0.6200000047683716},{"id":"https://openalex.org/keywords/deep-metric-learning","display_name":"deep metric learning","score":0.9490000009536743},{"id":"https://openalex.org/keywords/supervised-contrastive-learning","display_name":"supervised contrastive learning","score":0.8709999918937683},{"id":"https://openalex.org/keywords/hdbscan-clustering","display_name":"HDBSCAN clustering","score":0.8989999890327454}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5777999758720398},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.5619000196456909},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5590000152587891},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5212000012397766},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.48649999499320984},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.44909998774528503},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44530001282691956},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.44209998846054077},{"id":"https://openalex.org/C2780167933","wikidata":"https://www.wikidata.org/wiki/Q1550652","display_name":"Pulse (music)","level":3,"score":0.40700000524520874},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.40149998664855957},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3919999897480011},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3767000138759613},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36629998683929443},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3573000133037567},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.35190001130104065},{"id":"https://openalex.org/C87117476","wikidata":"https://www.wikidata.org/wiki/Q362383","display_name":"Cardinality (data modeling)","level":2,"score":0.3476000130176544},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.33379998803138733},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.30309998989105225},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2892000079154968},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28610000014305115},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C172321821","wikidata":"https://www.wikidata.org/wiki/Q7259685","display_name":"Pulse wave","level":3,"score":0.27549999952316284},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.25057","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.25057","pdf_url":"https://arxiv.org/pdf/2609.25057","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2609.25057","doi":"https://doi.org/10.48550/arxiv.2609.25057","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.25057","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.25057","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.25057","pdf_url":"https://arxiv.org/pdf/2609.25057","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7214066415.pdf","grobid_xml":"https://content.openalex.org/works/W7214066415.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W6979305743","https://openalex.org/W7213945215","https://openalex.org/W7133300886","https://openalex.org/W7165982007","https://openalex.org/W4210380380","https://openalex.org/W3174003812","https://openalex.org/W4320713243","https://openalex.org/W4385455659","https://openalex.org/W4288391508","https://openalex.org/W7064963399"],"abstract_inverted_index":{"Radar":[0],"pulse":[1,17,42],"de-interleaving":[2],"is":[3],"a":[4,92,110],"foundational":[5],"Electronic":[6],"Support":[7],"Measures":[8],"(ESM)":[9],"task":[10],"that":[11,99,113],"aims":[12],"to":[13],"separate":[14],"chronologically":[15],"interleaved":[16],"streams":[18],"from":[19],"multiple":[20],"non-cooperative":[21],"transmitters":[22],"under":[23,40],"unknown":[24],"emitter":[25],"cardinality":[26],"in":[27,67],"dense,":[28],"contested":[29],"electromagnetic":[30],"environments.":[31],"Classical":[32],"histogram":[33],"transforms":[34],"and":[35,51,63,125,149,157],"closed-world":[36],"deep":[37,68],"classifiers":[38],"degrade":[39],"severe":[41],"loss,":[43],"agile":[44],"Pulse":[45],"Repeti":[46],"tion":[47],"Interval":[48],"(PRI)":[49],"modulation,":[50],"spurious":[52],"clutter.":[53],"In":[54],"this":[55],"paper,":[56],"we":[57,90],"systematically":[58],"characterise":[59],"continuous":[60,93],"temporal":[61],"representations":[62],"physics-guided":[64,155],"model":[65],"selection":[66],"metric":[69],"learning":[70],"for":[71,81],"open-world":[72,82],"radar":[73],"de-interleaving.":[74],"Building":[75],"on":[76,146,161],"the":[77],"transformer-based":[78],"metric-learning":[79],"framework":[80],"deinterleaving":[83],"introduced":[84],"by":[85],"Gunn":[86],"et":[87],"al.":[88],"[1],":[89,116],"introduce":[91],"Time-of-Arrival":[94],"(ToA)":[95],"sinusoidal":[96],"positional":[97,120],"encoding":[98],"directly":[100],"models":[101],"physical":[102,142],"inter-pulse":[103],"durations":[104],"rather":[105],"than":[106],"ordinal":[107,119],"token":[108],"indices,":[109],"design":[111],"choice":[112],"contrasts":[114],"with":[115],"who":[117],"found":[118],"encodings":[121],"provided":[122],"no":[123],"benefit":[124],"omitted":[126],"them":[127],"entirely.":[128],"Neural":[129],"network":[130],"parameters":[131],"are":[132],"optimised":[133],"solely":[134],"via":[135],"Supervised":[136],"Contrastive":[137],"(SupCon)":[138],"learning,":[139],"while":[140],"scale-aware":[141],"domain":[143],"priors":[144],"based":[145],"PRI":[147],"Consistency":[148],"Angle-of-Arrival":[150],"(AoA)":[151],"continuity":[152],"serve":[153],"as":[154],"validation":[156],"checkpoint-selection":[158],"criteria":[159],"operating":[160],"unsupervised":[162],"HDBSCAN":[163],"cluster":[164],"assignments.":[165]},"counts_by_year":[],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2026-09-24T00:00:00"}
