{"id":"https://openalex.org/W2891032110","doi":"https://doi.org/10.1186/s13634-018-0577-1","title":"A Bayesian robust Kalman smoothing framework for state-space models with uncertain noise statistics","display_name":"A Bayesian robust Kalman smoothing framework for state-space models with uncertain noise statistics","publication_year":2018,"publication_date":"2018-09-06","ids":{"openalex":"https://openalex.org/W2891032110","doi":"https://doi.org/10.1186/s13634-018-0577-1","mag":"2891032110"},"language":"en","primary_location":{"id":"doi:10.1186/s13634-018-0577-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-018-0577-1","pdf_url":"https://asp-eurasipjournals.springeropen.com/track/pdf/10.1186/s13634-018-0577-1","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://asp-eurasipjournals.springeropen.com/track/pdf/10.1186/s13634-018-0577-1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053317035","display_name":"Roozbeh Dehghannasiri","orcid":"https://orcid.org/0000-0001-7413-3437"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Roozbeh Dehghannasiri","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073946580","display_name":"Xiaoning Qian","orcid":"https://orcid.org/0000-0002-4347-2476"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoning Qian","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112221423","display_name":"Edward R. Dougherty","orcid":null},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Edward R. Dougherty","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843, USA","institution_ids":["https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5053317035"],"corresponding_institution_ids":["https://openalex.org/I91045830"],"apc_list":{"value":1140,"currency":"GBP","value_usd":1398},"apc_paid":{"value":1140,"currency":"GBP","value_usd":1398},"fwci":1.0154,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.82838444,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"2018","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9979000091552734,"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"}},"topics":[{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9979000091552734,"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"}},{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9958999752998352,"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"}},{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9904999732971191,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/kalman-filter","display_name":"Kalman filter","score":0.7892938256263733},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6210160255432129},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5538569092750549},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5237736105918884},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5048907399177551},{"id":"https://openalex.org/keywords/bayesian-statistics","display_name":"Bayesian statistics","score":0.4831623435020447},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.47849148511886597},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4784103333950043},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46886757016181946},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4340571463108063},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.41669487953186035},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4021194875240326},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2665010094642639}],"concepts":[{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.7892938256263733},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6210160255432129},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5538569092750549},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5237736105918884},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5048907399177551},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.4831623435020447},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.47849148511886597},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4784103333950043},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46886757016181946},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4340571463108063},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.41669487953186035},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4021194875240326},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2665010094642639},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s13634-018-0577-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-018-0577-1","pdf_url":"https://asp-eurasipjournals.springeropen.com/track/pdf/10.1186/s13634-018-0577-1","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c4d1eaab4622493e9d7bfefab6a98058","is_oa":true,"landing_page_url":"https://doaj.org/article/c4d1eaab4622493e9d7bfefab6a98058","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"EURASIP Journal on Advances in Signal Processing, Vol 2018, Iss 1, Pp 1-17 (2018)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s13634-018-0577-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-018-0577-1","pdf_url":"https://asp-eurasipjournals.springeropen.com/track/pdf/10.1186/s13634-018-0577-1","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6499999761581421,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G3936902016","display_name":null,"funder_award_id":"1553281","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4552275141","display_name":null,"funder_award_id":"CCF-1553281","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2891032110.pdf","grobid_xml":"https://content.openalex.org/works/W2891032110.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W576301840","https://openalex.org/W1561649271","https://openalex.org/W1876120984","https://openalex.org/W1970886386","https://openalex.org/W1972155227","https://openalex.org/W1980356754","https://openalex.org/W1985495956","https://openalex.org/W2013891788","https://openalex.org/W2018295752","https://openalex.org/W2049898120","https://openalex.org/W2065736545","https://openalex.org/W2073557215","https://openalex.org/W2078806674","https://openalex.org/W2080118038","https://openalex.org/W2094227853","https://openalex.org/W2101792624","https://openalex.org/W2105934661","https://openalex.org/W2106409447","https://openalex.org/W2110012495","https://openalex.org/W2134330484","https://openalex.org/W2134717973","https://openalex.org/W2135664220","https://openalex.org/W2137586026","https://openalex.org/W2137813581","https://openalex.org/W2145834063","https://openalex.org/W2146485022","https://openalex.org/W2151107084","https://openalex.org/W2156064084","https://openalex.org/W2159605934","https://openalex.org/W2161129698","https://openalex.org/W2204230881","https://openalex.org/W2513723748","https://openalex.org/W2515453889","https://openalex.org/W2550588817","https://openalex.org/W2582188783","https://openalex.org/W2765295959","https://openalex.org/W2782180943","https://openalex.org/W2963672032","https://openalex.org/W3196810118","https://openalex.org/W4249925951"],"related_works":["https://openalex.org/W2964314781","https://openalex.org/W4389708677","https://openalex.org/W3006565005","https://openalex.org/W4287868071","https://openalex.org/W2611832276","https://openalex.org/W69468016","https://openalex.org/W2007093222","https://openalex.org/W4321613659","https://openalex.org/W2145178290","https://openalex.org/W3124172274"],"abstract_inverted_index":{"The":[0,62,75,136],"classical":[1],"Kalman":[2,42,78,90,123],"smoother":[3,43,79,91,154],"recursively":[4],"estimates":[5,48,139],"states":[6],"over":[7,131],"a":[8,65,70],"finite":[9],"time":[10],"window":[11,133],"using":[12,114],"all":[13],"observations":[14],"in":[15,127,142,155],"the":[16,24,27,54,58,81,88,93,103,106,115,120,128,132,143,148,151,156],"window.":[17],"In":[18,102],"this":[19],"paper,":[20],"we":[21],"assume":[22],"that":[23,49,86],"parameters":[25],"characterizing":[26],"second-order":[28],"statistics":[29,96,110],"of":[30,57,87,134,150],"process":[31,68],"and":[32,37,69,159],"observation":[33],"noise":[34,60,95,109,118],"are":[35,50,111,140],"unknown":[36,59],"propose":[38],"an":[39],"optimal":[40,51,76,121],"Bayesian":[41,66,72,77,122,137],"(OBKS)":[44],"to":[45,53],"obtain":[46],"smoothed":[47,138],"relative":[52],"posterior":[55,107],"distribution":[56],"parameters.":[61],"method":[63],"uses":[64],"innovation":[67],"posterior-based":[71],"orthogonality":[73],"principle.":[74],"possesses":[80],"same":[82],"forward-backward":[83],"structure":[84],"as":[85],"ordinary":[89,94],"with":[92],"replaced":[97],"by":[98],"their":[99],"effective":[100,108,117],"counterparts.":[101],"first":[104],"step,":[105],"computed.":[112],"Then,":[113],"obtained":[116,141],"statistics,":[119],"filter":[124],"is":[125],"run":[126],"forward":[129],"direction":[130],"observations.":[135],"backward":[144],"step.":[145],"We":[146],"validate":[147],"performance":[149],"proposed":[152],"robust":[153],"target":[157],"tracking":[158],"gene":[160],"regulatory":[161],"network":[162],"inference":[163],"problems.":[164]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
