{"id":"https://openalex.org/W7133545506","doi":"https://doi.org/10.1109/tsp.2026.3670448","title":"Label-Free Range-Based Indoor Tracking With Physics-Guided Deep State Space Model","display_name":"Label-Free Range-Based Indoor Tracking With Physics-Guided Deep State Space Model","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7133545506","doi":"https://doi.org/10.1109/tsp.2026.3670448"},"language":null,"primary_location":{"id":"doi:10.1109/tsp.2026.3670448","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2026.3670448","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/A5114246682","display_name":"Geng Wang","orcid":"https://orcid.org/0009-0004-2832-4207"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Geng Wang","raw_affiliation_strings":["School of Electrical and Computer Engineering, The University of Sydney, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0009-0004-2832-4207","affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, The University of Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128079865","display_name":"Peng Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I196829312","display_name":"La Trobe University","ror":"https://ror.org/01rxfrp27","country_code":"AU","type":"education","lineage":["https://openalex.org/I196829312"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Peng Cheng","raw_affiliation_strings":["Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia"],"raw_orcid":"https://orcid.org/0000-0003-1091-7894","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I196829312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053636716","display_name":"S. Li","orcid":null},"institutions":[{"id":"https://openalex.org/I1292875679","display_name":"Commonwealth Scientific and Industrial Research Organisation","ror":"https://ror.org/03qn8fb07","country_code":"AU","type":"government","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I4387156119"]},{"id":"https://openalex.org/I42894916","display_name":"Data61","ror":"https://ror.org/03q397159","country_code":"AU","type":"other","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I42894916","https://openalex.org/I4387156119"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shenghong Li","raw_affiliation_strings":["CSIRO DATA61, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0002-5516-9394","affiliations":[{"raw_affiliation_string":"CSIRO DATA61, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I42894916","https://openalex.org/I1292875679"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017910586","display_name":"Wei Xiang","orcid":"https://orcid.org/0000-0002-0608-065X"},"institutions":[{"id":"https://openalex.org/I196829312","display_name":"La Trobe University","ror":"https://ror.org/01rxfrp27","country_code":"AU","type":"education","lineage":["https://openalex.org/I196829312"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wei Xiang","raw_affiliation_strings":["Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia"],"raw_orcid":"https://orcid.org/0000-0002-0608-065X","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I196829312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043371405","display_name":"Branka Vucetic","orcid":"https://orcid.org/0000-0002-2700-2001"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Branka Vucetic","raw_affiliation_strings":["School of Electrical and Computer Engineering, The University of Sydney, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0002-2700-2001","affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, The University of Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yonghui Li","orcid":"https://orcid.org/0000-0001-7702-1123"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yonghui Li","raw_affiliation_strings":["School of Electrical and Computer Engineering, The University of Sydney, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0001-7702-1123","affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, The University of Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25677656,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"74","issue":null,"first_page":"1174","last_page":"1189"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.14139999449253082,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10860","display_name":"Speech and Audio Processing","score":0.14139999449253082,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.07039999961853027,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.060499999672174454,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/radar-tracker","display_name":"Radar tracker","score":0.4153999984264374},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.3898000121116638},{"id":"https://openalex.org/keywords/state-space","display_name":"State space","score":0.3626999855041504},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.3546999990940094},{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.33709999918937683},{"id":"https://openalex.org/keywords/nasa-deep-space-network","display_name":"NASA Deep Space Network","score":0.33480000495910645},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3330000042915344},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.3294999897480011},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.3163999915122986}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5958999991416931},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4300000071525574},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.4153999984264374},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.3898000121116638},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37220001220703125},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.3626999855041504},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.3546999990940094},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.33709999918937683},{"id":"https://openalex.org/C187107819","wikidata":"https://www.wikidata.org/wiki/Q835696","display_name":"NASA Deep Space Network","level":3,"score":0.33480000495910645},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3330000042915344},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.3294999897480011},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3125},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.29190000891685486},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2879999876022339},{"id":"https://openalex.org/C52918065","wikidata":"https://www.wikidata.org/wiki/Q230945","display_name":"State-space representation","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C119247159","wikidata":"https://www.wikidata.org/wiki/Q1366192","display_name":"System identification","level":3,"score":0.28600001335144043},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2833999991416931},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C2984536560","wikidata":"https://www.wikidata.org/wiki/Q818544","display_name":"State estimator","level":3,"score":0.2734000086784363},{"id":"https://openalex.org/C114996537","wikidata":"https://www.wikidata.org/wiki/Q4854529","display_name":"Colors of noise","level":3,"score":0.2694000005722046},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C154586513","wikidata":"https://www.wikidata.org/wiki/Q4420972","display_name":"Tracking system","level":3,"score":0.2590000033378601},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2026.3670448","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2026.3670448","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":[{"display_name":"Sustainable cities and communities","score":0.6731144189834595,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G485894664","display_name":"Low-cost Sensing Methods and Hybrid Learning Models","funder_award_id":"DP220101634","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"},{"id":"https://openalex.org/G5628963577","display_name":"Wireless Cellular Connectivity for Large Scale Critical Applications","funder_award_id":"DP210103410","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"}],"funders":[{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"indoor":[1,16,105,202],"tracking":[2,23,106,208],"is":[3],"essential":[4],"to":[5,32,52,92,132,211],"modern":[6],"location-based":[7],"services,":[8],"fundamentally":[9],"transforming":[10],"the":[11,90,117,130,148,161,184],"way":[12],"we":[13,65],"interact":[14],"with":[15,81],"environments.":[17],"Traditional":[18],"state":[19,70],"space":[20,71],"model":[21,72,79],"(SSM)-based":[22],"approaches":[24],"often":[25],"exhibit":[26],"limitations":[27],"in":[28,104,136],"complex":[29,134],"environments":[30],"due":[31],"their":[33,50],"reliance":[34],"on":[35],"fixed":[36,77],"and":[37,43,58,101,120,140,178],"overly":[38],"simplified":[39],"<italic":[40,44,122,175,180],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[41,45,123,176,181],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">transition</i>":[42],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">observation</i>":[46],"functions,":[47],"which":[48],"restricts":[49],"capability":[51],"adequately":[53],"capture":[54],"intricate":[55],"target":[56,138],"dynamics":[57,100,135],"measurement":[59,141],"uncertainties.":[60],"To":[61],"address":[62],"these":[63,76],"challenges,":[64],"propose":[66],"a":[67],"novel":[68],"deep":[69],"(DSSM)":[73],"that":[74],"augments":[75],"physics-based":[78],"functions":[80],"trainable":[82],"neural":[83],"networks":[84],"(NNs).":[85],"This":[86],"innovative":[87],"integration":[88],"enables":[89],"DSSM":[91,115,150],"effectively":[93],"learn":[94],"previously":[95],"unknown":[96],"or":[97,206],"inadequately":[98],"modeled":[99],"uncertainties":[102],"inherent":[103],"systems,":[107],"while":[108,127],"preserving":[109],"critical":[110],"physical":[111],"constraints.":[112],"Our":[113],"proposed":[114,149,185],"retains":[116],"structured":[118],"representation":[119],"Bayesian":[121],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">inference</i>":[124],"of":[125,172,192],"SSMs":[126],"significantly":[128],"improving":[129],"capacity":[131],"characterize":[133],"both":[137,173],"motion":[139],"errors.":[142],"By":[143],"leveraging":[144],"this":[145],"hybrid":[146],"structure,":[147],"facilitates":[151],"maximum":[152],"likelihood":[153],"parameter":[154],"learning":[155],"directly":[156],"from":[157,196],"range":[158],"measurements,":[159],"eliminating":[160],"need":[162],"for":[163,183],"ground":[164],"truth":[165],"data.":[166],"We":[167],"further":[168],"develop":[169],"inference":[170],"schemes":[171],"online":[174],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">filtering</i>":[177],"offline":[179],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">smoothing</i>":[182],"DSSM.":[186],"Extensive":[187],"evaluations":[188],"using":[189],"real-world":[190],"time":[191],"flight":[193],"(ToF)":[194],"measurements":[195],"two":[197],"datasets":[198],"across":[199],"five":[200],"diverse":[201],"scenarios":[203],"demonstrate":[204],"competitive":[205],"superior":[207],"performance":[209],"compared":[210],"other":[212],"state-of-the-art":[213],"methods.":[214]},"counts_by_year":[],"updated_date":"2026-06-22T08:00:12.763002","created_date":"2026-03-05T00:00:00"}