{"id":"https://openalex.org/W4400647124","doi":"https://doi.org/10.1109/iv55156.2024.10588496","title":"Mobile device\u2019s PDR Application Using CNN Based SpeedNet and GNSS Fusion","display_name":"Mobile device\u2019s PDR Application Using CNN Based SpeedNet and GNSS Fusion","publication_year":2024,"publication_date":"2024-06-02","ids":{"openalex":"https://openalex.org/W4400647124","doi":"https://doi.org/10.1109/iv55156.2024.10588496"},"language":"en","primary_location":{"id":"doi:10.1109/iv55156.2024.10588496","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iv55156.2024.10588496","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Intelligent Vehicles Symposium (IV)","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/A5101576021","display_name":"Yang Cheng","orcid":"https://orcid.org/0000-0002-6785-7776"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yang Tsu Cheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110367950","display_name":"LU En","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu Yang En","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100779080","display_name":"Jun Wu","orcid":"https://orcid.org/0000-0002-6325-8418"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu Ting Jun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5111257467","display_name":"Chiang Kai Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chiang Kai Wei","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101576021"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11321483,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2569","last_page":"2574"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13924","display_name":"Internet of Things and Social Network Interactions","score":0.8773000240325928,"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"}},"topics":[{"id":"https://openalex.org/T13924","display_name":"Internet of Things and Social Network Interactions","score":0.8773000240325928,"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/T12406","display_name":"IoT and GPS-based Vehicle Safety Systems","score":0.8418999910354614,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T13717","display_name":"Advanced Algorithms and Applications","score":0.7771999835968018,"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/gnss-applications","display_name":"GNSS applications","score":0.8416718244552612},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6820935010910034},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.515077531337738},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4643533527851105},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33672159910202026},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.23327693343162537},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.17497509717941284}],"concepts":[{"id":"https://openalex.org/C14279187","wikidata":"https://www.wikidata.org/wiki/Q5514012","display_name":"GNSS applications","level":3,"score":0.8416718244552612},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6820935010910034},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.515077531337738},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4643533527851105},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33672159910202026},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.23327693343162537},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.17497509717941284},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iv55156.2024.10588496","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iv55156.2024.10588496","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2106226434","https://openalex.org/W2279510040","https://openalex.org/W3090160518","https://openalex.org/W4306812399","https://openalex.org/W6695169759"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W4386936491","https://openalex.org/W3007931018","https://openalex.org/W2132659060","https://openalex.org/W2031992971","https://openalex.org/W3214791684","https://openalex.org/W2353265673","https://openalex.org/W2031175860","https://openalex.org/W2152662039"],"abstract_inverted_index":{"In":[0,17,186],"recent":[1],"years,":[2],"wearable":[3],"sensors":[4],"and":[5,56,85,200],"mobile":[6],"devices":[7],"have":[8],"become":[9],"popular":[10],"tools":[11],"in":[12,67,95,222],"the":[13,25,77,122,131,138,173,187],"field":[14],"of":[15,175,210],"positioning.":[16],"pedestrian":[18],"navigation,":[19],"Pedestrian":[20],"Dead":[21],"Reckoning":[22],"(PDR)":[23],"is":[24,212],"primary":[26],"algorithm,":[27],"with":[28],"numerous":[29],"previous":[30],"research":[31],"cases":[32],"available.":[33],"However,":[34],"traditional":[35,128,160],"PDR":[36,129,161],"algorithms'":[37],"stride":[38,62,78],"length":[39,63,79],"calculations":[40],"rely":[41],"on":[42],"empirical":[43],"formulas":[44],"that":[45,159],"are":[46],"influenced":[47],"by":[48,127],"factors":[49],"such":[50],"as":[51,193,218],"user":[52,123],"height,":[53],"walking":[54,57],"frequency,":[55],"habits.":[58],"Without":[59],"appropriate":[60],"parameters,":[61],"estimation":[64,108,118,157],"can":[65,120,203],"result":[66],"significant":[68],"errors,":[69],"leading":[70,88],"to":[71,89,110,154,179],"poor":[72],"positioning":[73],"outcomes.":[74],"Apart":[75],"from":[76],"calculation":[80],"issue,":[81],"IMUs":[82],"contain":[83],"bias":[84],"noise":[86],"themselves,":[87],"drift":[90,183],"errors":[91],"over":[92,184],"time,":[93],"especially":[94],"consumer-grade":[96],"IMUs.":[97],"To":[98],"address":[99,155],"these":[100],"issues,":[101],"this":[102,141],"study":[103,142],"introduces":[104],"a":[105,144],"CNN":[106],"velocity":[107,117],"model":[109,119],"calculate":[111],"users'":[112],"1D":[113],"velocity.":[114],"The":[115],"trained":[116],"overcome":[121],"dependency":[124],"issue":[125],"caused":[126],"because":[130],"training":[132],"data":[133],"covers":[134],"different":[135,223],"users.":[136],"For":[137],"heading":[139],"calculation,":[140],"employs":[143],"novel":[145],"9D":[146],"IMU":[147],"AHRS":[148],"algorithm":[149],"(Laidig":[150],"et":[151],"al.,":[152],"2022)":[153],"attitude":[156],"problems":[158],"cannot":[162],"handle":[163],"effectively":[164],"under":[165],"high":[166],"motion":[167],"conditions.":[168],"Finally,":[169],"incorporating":[170],"GNSS":[171,199],"through":[172],"principles":[174],"Extended":[176],"Kalman":[177],"Filter(EKF)":[178],"compensate":[180],"for":[181],"IMU\u2019s":[182],"time.":[185],"experiment,":[188],"we":[189],"use":[190],"NovAtel":[191],"Pwrpak":[192],"ground":[194],"truth.":[195],"It":[196],"contains":[197],"high-quality":[198],"IMU,":[201],"which":[202],"provide":[204],"reliable":[205],"reference":[206],"trajectory.":[207],"A":[208],"comparison":[209],"trajectories":[211],"conducted":[213],"using":[214],"Huawei":[215],"mate20":[216],"pro":[217],"our":[219],"smartphone":[220],"device":[221],"modes.":[224]},"counts_by_year":[],"updated_date":"2026-03-04T07:04:00.330322","created_date":"2025-10-10T00:00:00"}
