{"id":"https://openalex.org/W3108258528","doi":"https://doi.org/10.1109/icspcc50002.2020.9259469","title":"An Adaptive Variational Bayesian Algorithm for Measurement Loss for Underwater Navigation","display_name":"An Adaptive Variational Bayesian Algorithm for Measurement Loss for Underwater Navigation","publication_year":2020,"publication_date":"2020-08-21","ids":{"openalex":"https://openalex.org/W3108258528","doi":"https://doi.org/10.1109/icspcc50002.2020.9259469","mag":"3108258528"},"language":"en","primary_location":{"id":"doi:10.1109/icspcc50002.2020.9259469","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icspcc50002.2020.9259469","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085322499","display_name":"Haoqian Huang","orcid":"https://orcid.org/0000-0001-9948-9624"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haoqian Huang","raw_affiliation_strings":["College of Energy and Electrical Engineering, Hohai University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Energy and Electrical Engineering, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101860586","display_name":"Jiacheng Tang","orcid":"https://orcid.org/0000-0003-2103-6953"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiacheng Tang","raw_affiliation_strings":["College of Energy and Electrical Engineering, Hohai University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Energy and Electrical Engineering, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082142922","display_name":"Yuanfeng Jin","orcid":"https://orcid.org/0000-0002-3707-4527"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanfeng Jin","raw_affiliation_strings":["College of Energy and Electrical Engineering, Hohai University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Energy and Electrical Engineering, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5085322499"],"corresponding_institution_ids":["https://openalex.org/I163340411"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15698401,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"67","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9997000098228455,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9997000098228455,"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/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11698","display_name":"Underwater Acoustics Research","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.7712512612342834},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6702296137809753},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6392378211021423},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5987686514854431},{"id":"https://openalex.org/keywords/underwater","display_name":"Underwater","score":0.5706745982170105},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.5068939328193665},{"id":"https://openalex.org/keywords/state-information","display_name":"State information","score":0.4886346459388733},{"id":"https://openalex.org/keywords/fast-kalman-filter","display_name":"Fast Kalman filter","score":0.46605759859085083},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4449981153011322},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.38191112875938416},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3800547420978546},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.26092594861984253}],"concepts":[{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.7712512612342834},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6702296137809753},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6392378211021423},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5987686514854431},{"id":"https://openalex.org/C98083399","wikidata":"https://www.wikidata.org/wiki/Q3246517","display_name":"Underwater","level":2,"score":0.5706745982170105},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.5068939328193665},{"id":"https://openalex.org/C2985963534","wikidata":"https://www.wikidata.org/wiki/Q7603704","display_name":"State information","level":3,"score":0.4886346459388733},{"id":"https://openalex.org/C150679823","wikidata":"https://www.wikidata.org/wiki/Q5436946","display_name":"Fast Kalman filter","level":4,"score":0.46605759859085083},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4449981153011322},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.38191112875938416},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3800547420978546},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.26092594861984253},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icspcc50002.2020.9259469","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icspcc50002.2020.9259469","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8700000047683716,"id":"https://metadata.un.org/sdg/14","display_name":"Life below water"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1568586930","https://openalex.org/W1584350005","https://openalex.org/W1971572145","https://openalex.org/W2033237147","https://openalex.org/W2494349348","https://openalex.org/W2511273988","https://openalex.org/W2560743047","https://openalex.org/W2911615283","https://openalex.org/W2944846513","https://openalex.org/W2996068592","https://openalex.org/W3037166960"],"related_works":["https://openalex.org/W2182059946","https://openalex.org/W1580685205","https://openalex.org/W2320496747","https://openalex.org/W1488420340","https://openalex.org/W2164174227","https://openalex.org/W4206770590","https://openalex.org/W2375426323","https://openalex.org/W2363340733","https://openalex.org/W2349716249","https://openalex.org/W2103062922"],"abstract_inverted_index":{"The":[0,20,63],"marine":[1],"environment":[2],"is":[3,9,66],"changeable":[4],"and":[5,7,17,51,70],"complex,":[6],"it":[8],"difficult":[10],"but":[11],"indispensable":[12],"to":[13,54,76],"study":[14],"the":[15,28,47,57,60,77,80,84,93,97],"complex":[16],"time-varying":[18],"environment.":[19],"measurement":[21,61],"loss":[22],"has":[23],"an":[24,36],"effect":[25],"on":[26],"obtaining":[27],"high":[29],"accuracy":[30],"navigation":[31],"information.":[32],"This":[33],"paper":[34],"proposes":[35],"adaptive":[37],"variational":[38,48,81],"Bayesian":[39,49,82],"filter":[40,53],"(AVBF)":[41],"algorithm":[42],"which":[43],"takes":[44],"advantages":[45],"of":[46,59,79],"approach":[50],"Kalman":[52,99],"deal":[55],"with":[56,96],"problems":[58],"loss.":[62],"proposed":[64],"AVBF":[65,94],"proved":[67],"in":[68],"theory":[69],"verified":[71],"by":[72,92],"simulation":[73],"experiments.":[74],"Owing":[75],"characteristics":[78],"approach,":[83],"higher":[85],"precise":[86],"state":[87],"information":[88],"can":[89],"be":[90],"acquired":[91],"compared":[95],"traditional":[98],"filter.":[100]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
