{"id":"https://openalex.org/W4380628745","doi":"https://doi.org/10.1145/3585542.3585555","title":"An Efficient Batch Bayesian WIV Doppler and Bearing Estimator for 3D Target Tracking","display_name":"An Efficient Batch Bayesian WIV Doppler and Bearing Estimator for 3D Target Tracking","publication_year":2023,"publication_date":"2023-02-17","ids":{"openalex":"https://openalex.org/W4380628745","doi":"https://doi.org/10.1145/3585542.3585555"},"language":"en","primary_location":{"id":"doi:10.1145/3585542.3585555","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3585542.3585555","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on Digital Signal Processing","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/A5101735038","display_name":"Chang Du","orcid":"https://orcid.org/0009-0004-1280-2032"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chang Du","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, China"],"raw_orcid":"https://orcid.org/0009-0004-1280-2032","affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036754435","display_name":"Jinlong Ren","orcid":"https://orcid.org/0000-0002-3826-6256"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinlong Ren","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, China"],"raw_orcid":"https://orcid.org/0000-0002-3826-6256","affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017369696","display_name":"Guohua Wei","orcid":"https://orcid.org/0000-0001-7680-5188"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guohua Wei","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, China"],"raw_orcid":"https://orcid.org/0000-0001-7680-5188","affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101735038"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":0.1704,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.53915186,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"86","last_page":"94"},"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":0.9998999834060669,"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":0.9998999834060669,"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":0.9692000150680542,"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"}},{"id":"https://openalex.org/T11325","display_name":"Inertial Sensor and Navigation","score":0.9682999849319458,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7452178597450256},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5563539266586304},{"id":"https://openalex.org/keywords/observer","display_name":"Observer (physics)","score":0.5153211951255798},{"id":"https://openalex.org/keywords/bayes-estimator","display_name":"Bayes estimator","score":0.47551944851875305},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.4656292796134949},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4640445113182068},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.45176368951797485},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.43936464190483093},{"id":"https://openalex.org/keywords/bias-of-an-estimator","display_name":"Bias of an estimator","score":0.4289338290691376},{"id":"https://openalex.org/keywords/minimum-mean-square-error","display_name":"Minimum mean square error","score":0.4254522919654846},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.41705119609832764},{"id":"https://openalex.org/keywords/control-theory","display_name":"Control theory (sociology)","score":0.4060983657836914},{"id":"https://openalex.org/keywords/minimum-variance-unbiased-estimator","display_name":"Minimum-variance unbiased estimator","score":0.4026462435722351},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.36446449160575867},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.32589441537857056},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17705076932907104},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06573235988616943}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7452178597450256},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5563539266586304},{"id":"https://openalex.org/C2780704645","wikidata":"https://www.wikidata.org/wiki/Q9251458","display_name":"Observer (physics)","level":2,"score":0.5153211951255798},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.47551944851875305},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.4656292796134949},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4640445113182068},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.45176368951797485},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.43936464190483093},{"id":"https://openalex.org/C191393472","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Bias of an estimator","level":4,"score":0.4289338290691376},{"id":"https://openalex.org/C90652560","wikidata":"https://www.wikidata.org/wiki/Q11091747","display_name":"Minimum mean square error","level":3,"score":0.4254522919654846},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.41705119609832764},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.4060983657836914},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.4026462435722351},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.36446449160575867},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.32589441537857056},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17705076932907104},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06573235988616943},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3585542.3585555","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3585542.3585555","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on Digital Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G558847560","display_name":null,"funder_award_id":"61671059","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W178056938","https://openalex.org/W1513008779","https://openalex.org/W2005452565","https://openalex.org/W2008457674","https://openalex.org/W2042662106","https://openalex.org/W2089286422","https://openalex.org/W2120833567","https://openalex.org/W2159249588","https://openalex.org/W2783155546","https://openalex.org/W2891846050","https://openalex.org/W2897442870","https://openalex.org/W2939482490","https://openalex.org/W3084256199","https://openalex.org/W3109352503","https://openalex.org/W3183862565"],"related_works":["https://openalex.org/W2349547417","https://openalex.org/W2008532365","https://openalex.org/W4252929433","https://openalex.org/W1523673657","https://openalex.org/W3123889116","https://openalex.org/W4283751653","https://openalex.org/W2508380856","https://openalex.org/W2014640816","https://openalex.org/W4320482479","https://openalex.org/W1579371164"],"abstract_inverted_index":{"The":[0],"paper":[1],"focuses":[2],"on":[3,74],"the":[4,14,19,30,41,50,54,75,84,90,98,104,110,116,119,131],"3D":[5,144],"TMA":[6],"problem":[7],"of":[8,53,77,100,125],"a":[9,24],"tough":[10],"target-observer":[11],"geometry":[12],"where":[13],"observer":[15],"is":[16,21,47,70,128,134],"motionless":[17],"and":[18,36,39,80,103,139],"target":[20],"approaching":[22],"at":[23],"constant":[25],"speed.":[26],"We":[27],"first":[28],"formulate":[29],"pseudolinear":[31,113],"equations":[32],"using":[33],"bearings,":[34],"elevations":[35],"Doppler":[37],"frequency":[38],"derive":[40],"measurement":[42],"noise":[43,105],"covariance":[44],"matrix,":[45],"which":[46],"used":[48],"as":[49,97],"weighted":[51,112],"matrix":[52],"estimator,":[55],"much":[56],"more":[57],"complex":[58],"than":[59],"that":[60,89,130],"in":[61,136,143],"2D.":[62],"An":[63],"efficient":[64],"batch":[65],"Bayesian":[66],"filtered":[67],"WIV":[68],"estimator":[69,133],"then":[71],"proposed,":[72],"based":[73],"principle":[76],"instrumental":[78],"variables":[79],"mean":[81],"filtering":[82],"to":[83,94],"measurements.":[85],"Simulation":[86],"results":[87],"illustrate":[88],"estimating":[91,141],"bias":[92,138],"tends":[93],"be":[95],"small":[96,123],"number":[99],"instants":[101],"increases":[102],"fluctuation":[106],"decreases,":[107],"compared":[108],"with":[109],"basic":[111],"estimator.":[114],"And":[115],"RMSE":[117],"fits":[118],"Cram\u00e9r-Rao":[120],"Bound":[121],"for":[122],"levels":[124],"noise.":[126],"It":[127],"verified":[129],"proposed":[132],"effective":[135],"reducing":[137],"improving":[140],"accuracy":[142],"TMA.":[145]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
