{"id":"https://openalex.org/W4210945599","doi":"https://doi.org/10.1109/tsp.2022.3148540","title":"Exponential Mixture Density Based Approximation to Posterior Cram\u00e9r-Rao Lower Bound for Distributed Target Tracking","display_name":"Exponential Mixture Density Based Approximation to Posterior Cram\u00e9r-Rao Lower Bound for Distributed Target Tracking","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4210945599","doi":"https://doi.org/10.1109/tsp.2022.3148540"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2022.3148540","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2022.3148540","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-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/A5027840989","display_name":"Ye Yuan","orcid":"https://orcid.org/0000-0001-5845-0037"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ye Yuan","raw_affiliation_strings":["School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100339151","display_name":"Wei Yi","orcid":"https://orcid.org/0000-0001-9878-7048"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Yi","raw_affiliation_strings":["School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018292481","display_name":"Pramod K. Varshney","orcid":"https://orcid.org/0000-0003-4504-5088"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pramod K. Varshney","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA","institution_ids":["https://openalex.org/I70983195"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5027840989"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":2.785,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.91489844,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"70","issue":null,"first_page":"862","last_page":"877"},"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":0.9998000264167786,"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":0.9998000264167786,"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":0.9939000010490417,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9609000086784363,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6214063167572021},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6130945086479187},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.4905175566673279},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4546731412410736},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.42812108993530273},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3173289895057678},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.28049373626708984},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16215717792510986},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.15937280654907227}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6214063167572021},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6130945086479187},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.4905175566673279},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4546731412410736},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.42812108993530273},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3173289895057678},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28049373626708984},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16215717792510986},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.15937280654907227},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2022.3148540","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2022.3148540","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2772875376","display_name":null,"funder_award_id":"61871103","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4825825379","display_name":null,"funder_award_id":"ZYGX2020ZB029","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6534452397","display_name":null,"funder_award_id":"61771110","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8328824696","display_name":null,"funder_award_id":"U19B2017","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8978917354","display_name":null,"funder_award_id":"B17008","funder_id":"https://openalex.org/F4320327912","funder_display_name":"Higher Education Discipline Innovation Project"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327912","display_name":"Higher Education Discipline Innovation Project","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W95577512","https://openalex.org/W653761051","https://openalex.org/W1653176908","https://openalex.org/W1976486052","https://openalex.org/W1983890061","https://openalex.org/W1984197955","https://openalex.org/W1994684794","https://openalex.org/W1996363234","https://openalex.org/W2011457345","https://openalex.org/W2024522552","https://openalex.org/W2025543203","https://openalex.org/W2034663708","https://openalex.org/W2036881328","https://openalex.org/W2051257992","https://openalex.org/W2083932386","https://openalex.org/W2084109990","https://openalex.org/W2090562304","https://openalex.org/W2098613108","https://openalex.org/W2098694296","https://openalex.org/W2103604181","https://openalex.org/W2104130465","https://openalex.org/W2106360727","https://openalex.org/W2114864438","https://openalex.org/W2119668398","https://openalex.org/W2123211114","https://openalex.org/W2129078811","https://openalex.org/W2140242774","https://openalex.org/W2142315243","https://openalex.org/W2143833711","https://openalex.org/W2145629606","https://openalex.org/W2148234182","https://openalex.org/W2148681057","https://openalex.org/W2149074579","https://openalex.org/W2155068963","https://openalex.org/W2160337655","https://openalex.org/W2161917596","https://openalex.org/W2162488823","https://openalex.org/W2535464896","https://openalex.org/W2593819095","https://openalex.org/W2912137037","https://openalex.org/W2998168058","https://openalex.org/W3007664534","https://openalex.org/W3087034880","https://openalex.org/W3091642928","https://openalex.org/W3098430945","https://openalex.org/W3114888285","https://openalex.org/W4250589301","https://openalex.org/W6604239174","https://openalex.org/W6638495226","https://openalex.org/W6677777831","https://openalex.org/W6679532437"],"related_works":["https://openalex.org/W1979597421","https://openalex.org/W2007980826","https://openalex.org/W4245490552","https://openalex.org/W4225152035","https://openalex.org/W2061531152","https://openalex.org/W3002753104","https://openalex.org/W2077600819","https://openalex.org/W1587224694","https://openalex.org/W2911598644","https://openalex.org/W4289713060"],"abstract_inverted_index":{"The":[0,115,139],"posterior":[1],"Cramr-Rao":[2],"lower":[3],"bound":[4,190],"(PCRLB)":[5],"and":[6,17,61,96,112,129],"a":[7,159],"number":[8],"of":[9,52,143,154],"its":[10],"extensions":[11],"including":[12],"the":[13,18,36,45,50,53,57,89,110,126,130,133,137,152,155,167,171,185,189,192,202,211],"unconditional":[14],"PCRLB":[15,20,62,116,144,172],"(U-PCRLB)":[16],"conditional":[19],"(C-PCRLB)":[21],"have":[22],"been":[23],"widely":[24],"studied":[25],"in":[26,47,100,136],"multi-sensor":[27],"target":[28],"tracking":[29],"(MSTT).":[30],"Previous":[31],"MSTT":[32],"works":[33],"assume":[34],"that":[35,73,184],"measurements":[37,54],"are":[38,55,98,180],"conditionally":[39],"independent,":[40],"which":[41],"is":[42,145,198],"often":[43],"not":[44],"case":[46],"practice.":[48],"When":[49],"correlations":[51,80],"unknown,":[56],"standard":[58],"Bayes":[59],"update":[60],"computations":[63],"cannot":[64],"be":[65,118],"performed.":[66],"Inspired":[67],"by":[68,81],"geometric":[69],"average":[70],"(GA)":[71],"fusion":[72,76,107,208],"implements":[74],"data":[75,79,140],"with":[77],"unknown":[78],"employing":[82],"exponential":[83],"mixture":[84],"density":[85],"(EMD)":[86],"to":[87,94,151,164,170,182,188,201],"compute":[88,166],"global":[90],"posterior,":[91,149],"EMD-based":[92,148],"approximations":[93],"U-PCRLB":[95],"C-PCRLB":[97],"derived":[99],"this":[101],"paper":[102],"for":[103,173],"two":[104,121],"classic":[105],"distributed":[106],"architectures,":[108],"namely":[109],"hierarchical":[111],"consensus":[113],"architectures.":[114],"can":[117],"decomposed":[119],"into":[120],"parts,":[122],"one":[123],"coming":[124],"from":[125,132],"prior":[127],"information":[128,134,141],"other":[131],"contained":[135],"data.":[138],"part":[142],"approximated":[146],"via":[147],"leading":[150],"approximation":[153,169,187],"PCRLB.":[156],"We":[157],"present":[158],"sequential":[160],"Monte":[161],"Carlo":[162],"solution":[163],"recursively":[165],"proposed":[168,186],"nonlinear":[174],"non-Gaussian":[175],"estimation":[176,193],"problems.":[177],"Numerical":[178],"simulations":[179],"provided":[181],"show":[183],"on":[191,206],"mean":[194],"square":[195],"error":[196],"(MSE)":[197],"tighter":[199],"compared":[200],"existing":[203],"bounds":[204],"based":[205],"likelihood":[207],"obtained":[209],"under":[210],"measurement":[212],"independence":[213],"assumption.":[214]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
