{"id":"https://openalex.org/W2571015439","doi":"https://doi.org/10.1109/cdc.2016.7799103","title":"Process-noise adaptive particle filtering with dependent process and measurement noise","display_name":"Process-noise adaptive particle filtering with dependent process and measurement noise","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2571015439","doi":"https://doi.org/10.1109/cdc.2016.7799103","mag":"2571015439"},"language":"en","primary_location":{"id":"doi:10.1109/cdc.2016.7799103","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc.2016.7799103","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 55th Conference on Decision and Control (CDC)","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/A5090118616","display_name":"Karl Berntorp","orcid":"https://orcid.org/0000-0002-6809-6657"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Karl Berntorp","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004144091","display_name":"Stefano Di Cairano","orcid":"https://orcid.org/0000-0002-2363-2807"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stefano Di Cairano","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5090118616"],"corresponding_institution_ids":["https://openalex.org/I4210159266"],"apc_list":null,"apc_paid":null,"fwci":2.1424,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.91038403,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"30","issue":null,"first_page":"5434","last_page":"5439"},"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":0.9995999932289124,"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":0.9995999932289124,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9976999759674072,"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"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9937999844551086,"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/noise","display_name":"Noise (video)","score":0.8030102252960205},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.6423227190971375},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.6385955810546875},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6178098917007446},{"id":"https://openalex.org/keywords/value-noise","display_name":"Value noise","score":0.5983328223228455},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.569520890712738},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5562465786933899},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5506120920181274},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5095992088317871},{"id":"https://openalex.org/keywords/state-space","display_name":"State space","score":0.4548649191856384},{"id":"https://openalex.org/keywords/gradient-noise","display_name":"Gradient noise","score":0.4539196491241455},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.41341447830200195},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36938661336898804},{"id":"https://openalex.org/keywords/control-theory","display_name":"Control theory (sociology)","score":0.35414814949035645},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.30761146545410156},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26797783374786377},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.21939581632614136},{"id":"https://openalex.org/keywords/noise-floor","display_name":"Noise floor","score":0.21145465970039368},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18337059020996094},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.1506844162940979},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.11478433012962341}],"concepts":[{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.8030102252960205},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.6423227190971375},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.6385955810546875},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6178098917007446},{"id":"https://openalex.org/C182163834","wikidata":"https://www.wikidata.org/wiki/Q2926529","display_name":"Value noise","level":5,"score":0.5983328223228455},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.569520890712738},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5562465786933899},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5506120920181274},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5095992088317871},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.4548649191856384},{"id":"https://openalex.org/C200378446","wikidata":"https://www.wikidata.org/wiki/Q4147391","display_name":"Gradient noise","level":5,"score":0.4539196491241455},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.41341447830200195},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36938661336898804},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.35414814949035645},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.30761146545410156},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26797783374786377},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21939581632614136},{"id":"https://openalex.org/C187612029","wikidata":"https://www.wikidata.org/wiki/Q17083130","display_name":"Noise floor","level":4,"score":0.21145465970039368},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18337059020996094},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.1506844162940979},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.11478433012962341},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cdc.2016.7799103","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc.2016.7799103","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 55th Conference on Decision and Control (CDC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7900000214576721}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W113895413","https://openalex.org/W323911834","https://openalex.org/W386331423","https://openalex.org/W1808008750","https://openalex.org/W2005596676","https://openalex.org/W2016065197","https://openalex.org/W2019928247","https://openalex.org/W2062100941","https://openalex.org/W2082781540","https://openalex.org/W2095376999","https://openalex.org/W2096678341","https://openalex.org/W2111787305","https://openalex.org/W2129927244","https://openalex.org/W2143737806","https://openalex.org/W2148061324","https://openalex.org/W2148613679","https://openalex.org/W2149671441","https://openalex.org/W2156064084","https://openalex.org/W2165609874","https://openalex.org/W2167442255","https://openalex.org/W3104382201","https://openalex.org/W3121154744","https://openalex.org/W4300633630","https://openalex.org/W6676780147","https://openalex.org/W7030535387"],"related_works":["https://openalex.org/W3125397569","https://openalex.org/W4226451553","https://openalex.org/W2588855097","https://openalex.org/W2161471497","https://openalex.org/W2032021057","https://openalex.org/W2170781407","https://openalex.org/W2005333371","https://openalex.org/W3033631740","https://openalex.org/W2144739192","https://openalex.org/W3149431777"],"abstract_inverted_index":{"Knowledge":[0],"of":[1,34,64,80,84],"the":[2,17,58,62,65,81,85],"noise":[3,19,45,70],"distributions":[4],"is":[5],"typically":[6],"key":[7],"for":[8,55,91],"reliable":[9],"state":[10,59],"estimation.":[11],"However,":[12],"in":[13],"many":[14],"applications":[15],"only":[16,26],"measurement":[18],"can":[20],"be":[21],"determined":[22],"a":[23,50,92],"priori,":[24],"since":[25],"this":[27],"correspond":[28],"to":[29,39],"measurable":[30],"quantities.":[31],"Moreover,":[32],"modeling":[33],"physical":[35],"systems":[36],"often":[37],"leads":[38],"nonlinear":[40],"state-space":[41],"models":[42],"with":[43],"dependent":[44,69],"sources.":[46,71],"Here,":[47],"we":[48],"design":[49],"computationally-efficient":[51],"marginalized":[52],"particle":[53],"filter":[54],"jointly":[56],"estimating":[57],"trajectory":[60],"and":[61,77,89],"parameters":[63],"process":[66],"noise,":[67],"assuming":[68],"Our":[72],"approach":[73],"relies":[74],"on":[75],"marginalization":[76],"subsequent":[78],"update":[79],"sufficient":[82],"statistics":[83],"process-noise":[86],"parameters.":[87],"Results":[88],"comparisons":[90],"benchmark":[93],"example":[94],"indicate":[95],"that":[96],"our":[97],"method":[98],"gives":[99],"clear":[100],"improvements.":[101]},"counts_by_year":[{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
