{"id":"https://openalex.org/W4388593455","doi":"https://doi.org/10.1145/3625343.3625364","title":"Bayesian Multi-Target Tracking on Spatial Spectrum","display_name":"Bayesian Multi-Target Tracking on Spatial Spectrum","publication_year":2023,"publication_date":"2023-09-15","ids":{"openalex":"https://openalex.org/W4388593455","doi":"https://doi.org/10.1145/3625343.3625364"},"language":"en","primary_location":{"id":"doi:10.1145/3625343.3625364","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3625343.3625364","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics","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/A5044025253","display_name":"Xiaonan Xu","orcid":"https://orcid.org/0000-0002-0276-5766"},"institutions":[{"id":"https://openalex.org/I4210144142","display_name":"Hangzhou Institute of Applied Acoustics","ror":"https://ror.org/0473ary24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210144142"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaonan Xu","raw_affiliation_strings":["Hangzhou Institute of Applied Acoustics, China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Institute of Applied Acoustics, China","institution_ids":["https://openalex.org/I4210144142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103055816","display_name":"Shuanping Du","orcid":"https://orcid.org/0009-0003-5579-0599"},"institutions":[{"id":"https://openalex.org/I4210144142","display_name":"Hangzhou Institute of Applied Acoustics","ror":"https://ror.org/0473ary24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210144142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuanping Du","raw_affiliation_strings":["Hangzhou Institute of Applied Acoustics, China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Institute of Applied Acoustics, China","institution_ids":["https://openalex.org/I4210144142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013336524","display_name":"Yanyan An","orcid":"https://orcid.org/0000-0001-8002-3939"},"institutions":[{"id":"https://openalex.org/I4210144142","display_name":"Hangzhou Institute of Applied Acoustics","ror":"https://ror.org/0473ary24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210144142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanyan An","raw_affiliation_strings":["Hangzhou Institute of Applied Acoustics, China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Institute of Applied Acoustics, China","institution_ids":["https://openalex.org/I4210144142"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5044025253"],"corresponding_institution_ids":["https://openalex.org/I4210144142"],"apc_list":null,"apc_paid":null,"fwci":0.1748,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57506024,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9998000264167786,"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":0.9998000264167786,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9876000285148621,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11325","display_name":"Inertial Sensor and Navigation","score":0.9771999716758728,"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/computer-science","display_name":"Computer science","score":0.6723188757896423},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.6564503312110901},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6072299480438232},{"id":"https://openalex.org/keywords/track-before-detect","display_name":"Track-before-detect","score":0.6050982475280762},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6029062271118164},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5695028901100159},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5072300434112549},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.49169737100601196},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.47254180908203125},{"id":"https://openalex.org/keywords/radar-tracker","display_name":"Radar tracker","score":0.43392831087112427},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.42362311482429504},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41994133591651917},{"id":"https://openalex.org/keywords/tracking-system","display_name":"Tracking system","score":0.41531679034233093},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3435821533203125},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.32753658294677734},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.2501872181892395},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22444051504135132},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08041369915008545}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6723188757896423},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.6564503312110901},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6072299480438232},{"id":"https://openalex.org/C173028925","wikidata":"https://www.wikidata.org/wiki/Q7831461","display_name":"Track-before-detect","level":4,"score":0.6050982475280762},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6029062271118164},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5695028901100159},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5072300434112549},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.49169737100601196},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.47254180908203125},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.43392831087112427},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.42362311482429504},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41994133591651917},{"id":"https://openalex.org/C154586513","wikidata":"https://www.wikidata.org/wiki/Q4420972","display_name":"Tracking system","level":3,"score":0.41531679034233093},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3435821533203125},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.32753658294677734},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.2501872181892395},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22444051504135132},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08041369915008545},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3625343.3625364","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3625343.3625364","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics","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":11,"referenced_works":["https://openalex.org/W2024991751","https://openalex.org/W2096651706","https://openalex.org/W2126261784","https://openalex.org/W2129078811","https://openalex.org/W2137654406","https://openalex.org/W2147329751","https://openalex.org/W2150440166","https://openalex.org/W2155680787","https://openalex.org/W2156066484","https://openalex.org/W2156951132","https://openalex.org/W2173500095"],"related_works":["https://openalex.org/W2999948534","https://openalex.org/W2777039476","https://openalex.org/W2556174549","https://openalex.org/W2054685893","https://openalex.org/W3035724330","https://openalex.org/W2533372699","https://openalex.org/W2169003475","https://openalex.org/W1917254879","https://openalex.org/W1992429258","https://openalex.org/W2169278933"],"abstract_inverted_index":{"In":[0,37],"this":[1,38,65],"paper,":[2],"the":[3,13,18,24,32,40,45,49,56,67,80,91,106,116],"multi-target":[4,26,73],"tracking":[5,15,27,92],"problem":[6],"of":[7,34,48,72,119],"passive":[8,25],"monitoring":[9],"system":[10],"is":[11,21,29,51,59],"studied,":[12],"target":[14,50],"model":[16,58,71],"on":[17],"spatial":[19,46],"spectrum":[20,47],"established,":[22,77],"and":[23,55,69,78,90,113],"method":[28,107],"proposed":[30],"under":[31,115],"framework":[33],"Bayesian":[35,100],"processing.":[36],"method,":[39,89],"spectral":[41],"peak":[42],"corresponding":[43],"to":[44],"taken":[52],"as":[53],"contact":[54],"Gaussian":[57],"used":[60],"for":[61],"parametric":[62],"characterization.":[63],"On":[64],"basis,":[66],"observation":[68],"motion":[70],"parameters":[74],"can":[75,83,94,108],"be":[76,84,95],"then":[79],"iterative":[81],"estimation":[82],"carried":[85,96],"out":[86,97],"by":[87,99],"MAP":[88],"filtering":[93],"synchronously":[98],"method.":[101],"Simulation":[102],"results":[103],"show":[104],"that":[105],"track":[109],"multiple":[110,120],"targets":[111,121],"accurately":[112],"continuously":[114],"complex":[117],"condition":[118],"overlapping":[122],"each":[123],"other.":[124]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}