{"id":"https://openalex.org/W7133302080","doi":"https://doi.org/10.48550/arxiv.2603.01026","title":"RaUF: Learning the Spatial Uncertainty Field of Radar","display_name":"RaUF: Learning the Spatial Uncertainty Field of Radar","publication_year":2026,"publication_date":"2026-03-01","ids":{"openalex":"https://openalex.org/W7133302080","doi":"https://doi.org/10.48550/arxiv.2603.01026"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01026","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01026","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.2603.01026","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128015789","display_name":"Shengpeng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shengpeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076776007","display_name":"Kuangyu Wang","orcid":"https://orcid.org/0000-0002-9938-1469"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Kuangyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127914829","display_name":"Wei Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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/T11312","display_name":"Soil Moisture and Remote Sensing","score":0.23280000686645508,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11312","display_name":"Soil Moisture and Remote Sensing","score":0.23280000686645508,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11234","display_name":"Precipitation Measurement and Analysis","score":0.17739999294281006,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.11640000343322754,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/spurious-relationship","display_name":"Spurious relationship","score":0.6870999932289124},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5320000052452087},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4878999888896942},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4740999937057495},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.4668999910354614},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4562000036239624},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.43560001254081726},{"id":"https://openalex.org/keywords/complementarity","display_name":"Complementarity (molecular biology)","score":0.41620001196861267},{"id":"https://openalex.org/keywords/aliasing","display_name":"Aliasing","score":0.39079999923706055}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7135999798774719},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.6870999932289124},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5320000052452087},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4878999888896942},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4740999937057495},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.4668999910354614},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4562000036239624},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.43560001254081726},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42089998722076416},{"id":"https://openalex.org/C202269582","wikidata":"https://www.wikidata.org/wiki/Q2644277","display_name":"Complementarity (molecular biology)","level":2,"score":0.41620001196861267},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4025000035762787},{"id":"https://openalex.org/C4069607","wikidata":"https://www.wikidata.org/wiki/Q868732","display_name":"Aliasing","level":3,"score":0.39079999923706055},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.383899986743927},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.3828999996185303},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.38040000200271606},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.35109999775886536},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C2778559676","wikidata":"https://www.wikidata.org/wiki/Q1334213","display_name":"Doppler radar","level":3,"score":0.3400000035762787},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3276999890804291},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.2964000105857849},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.29010000824928284},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.28290000557899475},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.2599000036716461}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01026","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01026","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.2603.01026","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01026","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/13","display_name":"Climate action","score":0.672132134437561}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Millimeter-wave":[0],"radar":[1,70],"offers":[2],"unique":[3],"advantages":[4],"in":[5],"adverse":[6],"weather":[7],"but":[8],"suffers":[9],"from":[10],"low":[11],"spatial":[12,27,63,110,135],"fidelity,":[13],"severe":[14],"azimuth":[15],"ambiguity,":[16],"and":[17,48,112,126,149],"clutter-induced":[18],"spurious":[19,117],"returns.":[20],"Existing":[21],"methods":[22],"mainly":[23],"focus":[24],"on":[25,123],"improving":[26],"perception":[28,54],"effectiveness":[29],"via":[30],"coarse-to-fine":[31],"cross-modal":[32],"supervision,":[33],"yet":[34],"often":[35],"overlook":[36],"the":[37,106,146],"ambiguous":[38],"feature-to-label":[39,81],"mapping,":[40,82],"which":[41],"may":[42],"lead":[43],"to":[44,52],"ill-posed":[45],"geometric":[46],"inference":[47],"pose":[49],"fundamental":[50],"challenges":[51],"downstream":[53,141],"tasks.":[55],"In":[56],"this":[57],"work,":[58],"we":[59,83,97],"propose":[60,98],"RaUF,":[61],"a":[62,99],"uncertainty":[64],"field":[65],"learning":[66],"framework":[67],"that":[68,89,104,130],"models":[69],"measurements":[71],"through":[72],"their":[73],"physically":[74],"grounded":[75],"anisotropic":[76,86],"properties.":[77],"To":[78,93],"resolve":[79],"conflicting":[80],"design":[84],"an":[85],"probabilistic":[87],"model":[88],"learns":[90],"fine-grained":[91],"uncertainty.":[92,139],"further":[94,144],"enhance":[95],"reliability,":[96],"Bidirectional":[100],"Domain":[101],"Attention":[102],"mechanism":[103],"exploits":[105],"mutual":[107],"complementarity":[108],"between":[109],"structure":[111],"Doppler":[113],"consistency,":[114],"effectively":[115],"suppressing":[116],"or":[118],"multipath-induced":[119],"reflections.":[120],"Extensive":[121],"experiments":[122],"public":[124],"benchmarks":[125],"real-world":[127,155],"datasets":[128],"demonstrate":[129],"RaUF":[131,152],"delivers":[132],"highly":[133],"reliable":[134],"detections":[136],"with":[137],"well-calibrated":[138],"Moreover,":[140],"case":[142],"studies":[143],"validate":[145],"enhanced":[147],"reliability":[148],"scalability":[150],"of":[151],"under":[153],"challenging":[154],"driving":[156],"scenarios.":[157]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-04T00:00:00"}
