{"id":"https://openalex.org/W2714335099","doi":"https://doi.org/10.1109/icassp.2017.7952949","title":"A dynamic Bayesian nonparametric model for blind calibration of sensor networks","display_name":"A dynamic Bayesian nonparametric model for blind calibration of sensor networks","publication_year":2017,"publication_date":"2017-03-01","ids":{"openalex":"https://openalex.org/W2714335099","doi":"https://doi.org/10.1109/icassp.2017.7952949","mag":"2714335099"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2017.7952949","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7952949","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5018463611","display_name":"Jielong Yang","orcid":"https://orcid.org/0000-0001-5853-6316"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Jielong Yang","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046850399","display_name":"Wee Peng Tay","orcid":"https://orcid.org/0000-0002-1543-195X"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Wee Peng Tay","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100681425","display_name":"Xionghu Zhong","orcid":"https://orcid.org/0000-0002-7533-2347"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Xionghu Zhong","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"57","issue":null,"first_page":"4207","last_page":"4211"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.994700014591217,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.994700014591217,"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":0.992900013923645,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9876999855041504,"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/computer-science","display_name":"Computer science","score":0.680745542049408},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6052938103675842},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.6005231142044067},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5866433382034302},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5467149615287781},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5356528162956238},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.5018632411956787},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4967239499092102},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4897156059741974},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4827808439731598},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.47523024678230286},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.42788317799568176},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42570385336875916},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3860376477241516},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22717836499214172},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15042522549629211},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.09579071402549744}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.680745542049408},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6052938103675842},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6005231142044067},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5866433382034302},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5467149615287781},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5356528162956238},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.5018632411956787},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4967239499092102},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4897156059741974},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4827808439731598},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.47523024678230286},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.42788317799568176},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42570385336875916},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3860376477241516},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22717836499214172},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15042522549629211},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.09579071402549744},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2017.7952949","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7952949","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1503398984","https://openalex.org/W2007641398","https://openalex.org/W2011760672","https://openalex.org/W2049683924","https://openalex.org/W2071079847","https://openalex.org/W2071975411","https://openalex.org/W2074719413","https://openalex.org/W2109646819","https://openalex.org/W2115245044","https://openalex.org/W2118036030","https://openalex.org/W2138863982","https://openalex.org/W2146414958","https://openalex.org/W2152139894","https://openalex.org/W2165232124","https://openalex.org/W2171092999","https://openalex.org/W2462796524","https://openalex.org/W3104490327","https://openalex.org/W6677407863","https://openalex.org/W6682754680","https://openalex.org/W6824062597"],"related_works":["https://openalex.org/W3024740485","https://openalex.org/W1517763961","https://openalex.org/W2133543800","https://openalex.org/W3122927555","https://openalex.org/W2494929626","https://openalex.org/W27102384","https://openalex.org/W3161345512","https://openalex.org/W1990075812","https://openalex.org/W4378501308","https://openalex.org/W2952528636"],"abstract_inverted_index":{"In":[0],"the":[1,7,33,56,61,66,85,101,114,128],"sensor":[2,37,67,102,129],"network":[3],"blind":[4,147],"calibration":[5],"problem,":[6,28],"gains":[8,68,130],"and":[9,69,97,131,136,145],"offsets":[10,70,132],"of":[11,18],"sensors":[12,77],"are":[13,39,71,91,98],"estimated":[14],"from":[15],"noisy":[16],"observations":[17,38],"unknown":[19,57],"underlying":[20,58,87],"signals.":[21],"This":[22],"is":[23],"in":[24],"general":[25],"a":[26,47,95,106],"non-identifiable":[27],"unless":[29],"restrictive":[30],"assumptions":[31],"on":[32,113],"signal":[34],"subspace":[35],"or":[36],"imposed.":[40],"To":[41],"overcome":[42],"these":[43],"assumptions,":[44],"we":[45],"propose":[46],"dynamic":[48],"Bayesian":[49],"nonparametric":[50],"model.":[51],"We":[52,104],"show":[53],"that":[54,123,141],"if":[55],"signals":[59],"follow":[60],"first-order":[62],"auto-regressive":[63],"process,":[64],"then":[65,146],"identifiable.":[72],"Furthermore,":[73],"our":[74,124],"model":[75],"allows":[76],"to":[78],"form":[79],"clusters,":[80],"where":[81],"each":[82],"cluster":[83],"observes":[84],"same":[86],"signal.":[88],"The":[89],"clusters":[90],"however":[92],"not":[93],"known":[94],"priori,":[96],"learned":[99],"through":[100],"data.":[103],"present":[105],"block":[107],"Gibbs":[108],"sampling":[109,118],"inference":[110],"method":[111],"based":[112],"forward":[115],"filtering":[116],"backward":[117],"algorithm.":[119],"Simulation":[120],"results":[121],"suggest":[122],"approach":[125],"can":[126],"estimate":[127],"with":[133],"good":[134],"accuracy,":[135],"performs":[137],"better":[138],"than":[139],"methods":[140],"first":[142],"perform":[143],"clustering":[144],"calibration.":[148]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}