{"id":"https://openalex.org/W3090641107","doi":"https://doi.org/10.1109/ccta41146.2020.9206312","title":"Functional Replicas of Proprietary Three-Axis Attitude Sensors via LSTM Neural Networks","display_name":"Functional Replicas of Proprietary Three-Axis Attitude Sensors via LSTM Neural Networks","publication_year":2020,"publication_date":"2020-08-01","ids":{"openalex":"https://openalex.org/W3090641107","doi":"https://doi.org/10.1109/ccta41146.2020.9206312","mag":"3090641107"},"language":"en","primary_location":{"id":"doi:10.1109/ccta41146.2020.9206312","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccta41146.2020.9206312","pdf_url":null,"source":{"id":"https://openalex.org/S4306498667","display_name":"2020 IEEE Conference on Control Technology and Applications (CCTA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Conference on Control Technology and Applications (CCTA)","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/A5031351839","display_name":"Hao Fu","orcid":"https://orcid.org/0000-0002-1791-3311"},"institutions":[{"id":"https://openalex.org/I4210116723","display_name":"Robotics Research (United States)","ror":"https://ror.org/020w2fr77","country_code":"US","type":"company","lineage":["https://openalex.org/I4210116723"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hao Fu","raw_affiliation_strings":["Dept. of ECE, Control/Robotics Research Laboratory (CRRL), NYU Tandon School of Engg. (Polytechnic Institute), Brooklyn, NY, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of ECE, Control/Robotics Research Laboratory (CRRL), NYU Tandon School of Engg. (Polytechnic Institute), Brooklyn, NY, USA","institution_ids":["https://openalex.org/I4210116723"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054769060","display_name":"P. Krishnamurthy","orcid":"https://orcid.org/0000-0001-8264-7972"},"institutions":[{"id":"https://openalex.org/I4210116723","display_name":"Robotics Research (United States)","ror":"https://ror.org/020w2fr77","country_code":"US","type":"company","lineage":["https://openalex.org/I4210116723"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prashanth Krishnamurthy","raw_affiliation_strings":["Dept. of ECE, Control/Robotics Research Laboratory (CRRL), NYU Tandon School of Engg. (Polytechnic Institute), Brooklyn, NY, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of ECE, Control/Robotics Research Laboratory (CRRL), NYU Tandon School of Engg. (Polytechnic Institute), Brooklyn, NY, USA","institution_ids":["https://openalex.org/I4210116723"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082413942","display_name":"Farshad Khorrami","orcid":"https://orcid.org/0000-0002-8418-004X"},"institutions":[{"id":"https://openalex.org/I4210116723","display_name":"Robotics Research (United States)","ror":"https://ror.org/020w2fr77","country_code":"US","type":"company","lineage":["https://openalex.org/I4210116723"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Farshad Khorrami","raw_affiliation_strings":["Dept. of ECE, Control/Robotics Research Laboratory (CRRL), NYU Tandon School of Engg. (Polytechnic Institute), Brooklyn, NY, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of ECE, Control/Robotics Research Laboratory (CRRL), NYU Tandon School of Engg. (Polytechnic Institute), Brooklyn, NY, USA","institution_ids":["https://openalex.org/I4210116723"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5031351839"],"corresponding_institution_ids":["https://openalex.org/I4210116723"],"apc_list":null,"apc_paid":null,"fwci":2.0179,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.87743985,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"70","last_page":"75"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11325","display_name":"Inertial Sensor and Navigation","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11325","display_name":"Inertial Sensor and Navigation","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9976999759674072,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9911999702453613,"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/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.9467218518257141},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6809560656547546},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.6614875793457031},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5813843011856079},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5746304392814636},{"id":"https://openalex.org/keywords/quaternion","display_name":"Quaternion","score":0.5746064186096191},{"id":"https://openalex.org/keywords/units-of-measurement","display_name":"Units of measurement","score":0.5459995269775391},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5118412375450134},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.49467870593070984},{"id":"https://openalex.org/keywords/attitude-and-heading-reference-system","display_name":"Attitude and heading reference system","score":0.4585880935192108},{"id":"https://openalex.org/keywords/gyroscope","display_name":"Gyroscope","score":0.4543116092681885},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.36410626769065857},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.19826966524124146},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08256351947784424}],"concepts":[{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.9467218518257141},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6809560656547546},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.6614875793457031},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5813843011856079},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5746304392814636},{"id":"https://openalex.org/C200127275","wikidata":"https://www.wikidata.org/wiki/Q173853","display_name":"Quaternion","level":2,"score":0.5746064186096191},{"id":"https://openalex.org/C151233233","wikidata":"https://www.wikidata.org/wiki/Q47574","display_name":"Units of measurement","level":2,"score":0.5459995269775391},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5118412375450134},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.49467870593070984},{"id":"https://openalex.org/C204114589","wikidata":"https://www.wikidata.org/wiki/Q4248887","display_name":"Attitude and heading reference system","level":3,"score":0.4585880935192108},{"id":"https://openalex.org/C158488048","wikidata":"https://www.wikidata.org/wiki/Q483400","display_name":"Gyroscope","level":2,"score":0.4543116092681885},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.36410626769065857},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.19826966524124146},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08256351947784424},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"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/ccta41146.2020.9206312","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccta41146.2020.9206312","pdf_url":null,"source":{"id":"https://openalex.org/S4306498667","display_name":"2020 IEEE Conference on Control Technology and Applications (CCTA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Conference on Control Technology and Applications (CCTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W592904438","https://openalex.org/W1498436455","https://openalex.org/W1581808154","https://openalex.org/W1603631609","https://openalex.org/W1815076433","https://openalex.org/W1965784613","https://openalex.org/W2010305093","https://openalex.org/W2064675550","https://openalex.org/W2066578108","https://openalex.org/W2087473717","https://openalex.org/W2091112773","https://openalex.org/W2096533821","https://openalex.org/W2099159105","https://openalex.org/W2108742341","https://openalex.org/W2112364710","https://openalex.org/W2112408600","https://openalex.org/W2117011443","https://openalex.org/W2133696575","https://openalex.org/W2152699771","https://openalex.org/W2157331557","https://openalex.org/W2162868849","https://openalex.org/W2172198266","https://openalex.org/W2322441453","https://openalex.org/W2774740309","https://openalex.org/W2790374560","https://openalex.org/W2919115771","https://openalex.org/W2921870669","https://openalex.org/W2963883751","https://openalex.org/W3004572204","https://openalex.org/W4237444302","https://openalex.org/W6638545294","https://openalex.org/W6674304311","https://openalex.org/W6679713454"],"related_works":["https://openalex.org/W1989565895","https://openalex.org/W2132329956","https://openalex.org/W2375750355","https://openalex.org/W4317796711","https://openalex.org/W2802037228","https://openalex.org/W2016229664","https://openalex.org/W4387716091","https://openalex.org/W2384506543","https://openalex.org/W2789530741","https://openalex.org/W2388930037"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"machine-learning-based":[3],"tools":[4],"are":[5,56],"utilized":[6],"to":[7,85,88,104,114,148,154,166],"learn":[8],"a":[9,15,27,136,150],"model":[10],"of":[11,14,110,128],"the":[12,39,48,102,107,111,116,120,126,156,160,163,176],"functionality":[13],"commercial":[16],"chip":[17,103],"with":[18,35],"built-in":[19,108],"signal":[20],"processing":[21],"and":[22,42,68,138],"sensor":[23,122],"fusion":[24],"algorithms.":[25],"Specifically,":[26],"fully":[28],"integrated":[29],"9-axis":[30,121],"Inertial":[31],"Measurement":[32],"Unit":[33],"(IMU)":[34],"embedded":[36],"algorithms":[37,109],"providing":[38],"three-axis":[40],"attitude":[41,91,118],"corresponding":[43],"quaternion":[44],"by":[45,131],"fusing":[46,59],"all":[47,73],"sensors":[49,161],"is":[50],"considered.":[51],"Traditionally,":[52],"extended":[53],"Kalman":[54,75],"filters":[55],"used":[57],"for":[58,72,106],"IMU":[60,112,165],"sensors;":[61],"however,":[62],"subtle":[63],"algorithmic":[64],"fixes":[65],"(e.g.,":[66],"magnetic":[67,82],"angular":[69],"alignment":[70],"calibration":[71],"sensors,":[74],"filter":[76],"tuning,":[77],"temperature":[78],"drift":[79],"compensation,":[80],"dynamic":[81],"effects)":[83],"need":[84],"be":[86],"deployed":[87],"attain":[89],"precise":[90],"(especially":[92],"heading).":[93],"A":[94],"recurrent":[95],"neural":[96],"network":[97],"(RNN)":[98],"was":[99],"trained":[100],"using":[101],"substitute":[105],"Chip":[113],"output":[115],"approximate":[117],"given":[119],"data.":[123],"We":[124],"show":[125],"efficacy":[127],"our":[129],"approach":[130],"mounting":[132],"two":[133],"IMUs":[134],"on":[135,162],"board":[137],"utilize":[139],"one":[140],"IMU,":[141],"which":[142],"has":[143],"its":[144],"own":[145],"internal":[146],"algorithms,":[147],"train":[149],"machine":[151],"learning":[152],"system":[153],"fuse":[155],"raw":[157],"data":[158],"from":[159],"second":[164],"generate":[167],"comparable":[168],"accuracy":[169],"(and":[170],"in":[171],"some":[172],"cases,":[173],"even":[174],"outperform":[175],"original":[177],"IMU).":[178]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}