{"id":"https://openalex.org/W7171565457","doi":"https://doi.org/10.48550/arxiv.2607.24608","title":"Attribution and Uncertainty Behavior of Learned Residual Gyro Correction for Gyro-Stellar Estimation","display_name":"Attribution and Uncertainty Behavior of Learned Residual Gyro Correction for Gyro-Stellar Estimation","publication_year":2026,"publication_date":"2026-07-27","ids":{"openalex":"https://openalex.org/W7171565457","doi":"https://doi.org/10.48550/arxiv.2607.24608"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.24608","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.24608","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,"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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.24608","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042862102","display_name":"Mariela De Lucas \u00c1lvarez","orcid":"https://orcid.org/0000-0003-0846-4507"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"\u00c1lvarez, Mariela De Lucas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025962742","display_name":"Melvin Laux","orcid":"https://orcid.org/0000-0003-3517-7386"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Laux, Melvin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125502849","display_name":"Arthur de Freitas Precht","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Precht, Arthur de Freitas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061615268","display_name":"Maurice Martin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Martin, Maurice","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093795991","display_name":"Edoardo Caroselli","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Caroselli, Edoardo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143800093","display_name":"Frank Kirchner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kirchner, Frank","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5013678860","display_name":"Alexander Fabisch","orcid":"https://orcid.org/0000-0003-2824-7956"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fabisch, Alexander","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T11325","display_name":"Inertial Sensor and Navigation","score":0.8896999955177307,"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/T11325","display_name":"Inertial Sensor and Navigation","score":0.8896999955177307,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.029899999499320984,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.011699999682605267,"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/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.8208000063896179},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.663100004196167},{"id":"https://openalex.org/keywords/measurement-uncertainty","display_name":"Measurement uncertainty","score":0.6238999962806702},{"id":"https://openalex.org/keywords/uncertainty-analysis","display_name":"Uncertainty analysis","score":0.45559999346733093},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.4311999976634979},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.38960000872612},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3529999852180481},{"id":"https://openalex.org/keywords/perturbation","display_name":"Perturbation (astronomy)","score":0.3440999984741211}],"concepts":[{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.8208000063896179},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.663100004196167},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.6238999962806702},{"id":"https://openalex.org/C177803969","wikidata":"https://www.wikidata.org/wiki/Q29205","display_name":"Uncertainty analysis","level":2,"score":0.45559999346733093},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44369998574256897},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.4311999976634979},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42649999260902405},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.38960000872612},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35499998927116394},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3529999852180481},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3221000134944916},{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C176147448","wikidata":"https://www.wikidata.org/wiki/Q1889114","display_name":"Sensitivity analysis","level":3,"score":0.320499986410141},{"id":"https://openalex.org/C19619285","wikidata":"https://www.wikidata.org/wiki/Q196372","display_name":"Observational error","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.30309998989105225},{"id":"https://openalex.org/C123614077","wikidata":"https://www.wikidata.org/wiki/Q1364905","display_name":"Propagation of uncertainty","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C158488048","wikidata":"https://www.wikidata.org/wiki/Q483400","display_name":"Gyroscope","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2806999981403351},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27149999141693115},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.2621999979019165},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.2612000107765198},{"id":"https://openalex.org/C36299963","wikidata":"https://www.wikidata.org/wiki/Q1369844","display_name":"Observability","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.24608","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.24608","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,"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.24608","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.24608","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,"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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"work":[1,199],"investigates":[2],"uncertainty":[3,61,104,117,150,172,198,205,234],"decomposition":[4,108],"and":[5,34,54,79,84,90,103,116,126,132,142,159,196,202,212,229,238],"explainability":[6],"in":[7,81],"a":[8,44,107,174],"deep":[9],"learning-based":[10,225],"framework":[11],"for":[12,235],"gyroscope":[13,33],"bias":[14,39],"correction.":[15],"A":[16],"1-D":[17],"Convolutional":[18],"Neural":[19],"Network":[20],"is":[21,62,74,162,206],"trained":[22,69,75],"to":[23,43,99],"predict":[24],"residual":[25],"angular":[26],"rate":[27],"corrections":[28,40,53],"from":[29],"multi-sensor":[30],"inputs,":[31],"including":[32],"star":[35],"tracker":[36],"measurements.":[37],"The":[38,48,71,216],"are":[41,97],"sent":[42],"flight-representative":[45],"Gyro-Stellar":[46],"Estimator.":[47],"network":[49],"produces":[50],"both":[51,82,100],"mean":[52],"input-dependent":[55],"(heteroscedastic)":[56],"aleatoric":[57,141,149,195],"uncertainty,":[58],"while":[59],"epistemic":[60,143,171,197,204],"estimated":[63],"via":[64],"an":[65],"ensemble":[66],"of":[67,109,223,233],"independently":[68],"models.":[70],"proposed":[72],"approach":[73],"under":[76],"nominal":[77,83,211],"conditions":[78],"evaluated":[80],"structured":[85,135],"perturbations":[86,136],"that":[87,112,148,177,186,194,203],"include":[88],"additive":[89],"temporally":[91],"correlated":[92],"noise.":[93],"Gradient-based":[94],"attribution":[95,121],"methods":[96],"applied":[98],"the":[101,110,138,156,160,168,181,187,221,231],"correction":[102],"outputs,":[105],"enabling":[106],"evidence":[111],"drives":[113],"state":[114,226],"updates":[115],"estimates.":[118],"By":[119],"aggregating":[120],"patterns":[122],"across":[123,165],"rotational":[124],"axes":[125],"regimes,":[127],"we":[128],"reveal":[129],"axis-specific":[130],"behaviors":[131],"characterize":[133],"how":[134],"influence":[137],"collaboration":[139],"between":[140,210],"uncertainty.":[144],"Uncertainty":[145],"analysis":[146],"shows":[147],"increases":[151],"with":[152],"perturbation":[153],"intensity,":[154],"but":[155],"distributions":[157],"overlap":[158],"calibration":[161],"not":[163],"consistent":[164],"regimes.":[166],"On":[167],"other":[169],"hand,":[170],"gives":[173],"clear":[175],"signal":[176],"gets":[178],"clearer":[179],"as":[180],"distributional":[182],"shift":[183],"happens,":[184],"showing":[185],"models":[188],"disagree":[189],"more.":[190],"These":[191],"results":[192,217],"show":[193],"well":[200],"together":[201],"better":[207],"at":[208],"distinguishing":[209],"perturbed":[213],"operating":[214],"conditions.":[215],"provide":[218],"insight":[219],"into":[220],"behavior":[222],"hybrid":[224],"estimation":[227],"components":[228],"motivate":[230],"use":[232],"downstream":[236],"monitoring":[237],"fault":[239],"detection.":[240]},"counts_by_year":[],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2026-07-29T00:00:00"}
