{"id":"https://openalex.org/W4396629980","doi":"https://doi.org/10.1145/3654446.3654467","title":"Multi-extended Targets PHD Filtering Tracking Algorithm with Unknown Measurement Noise Distribution","display_name":"Multi-extended Targets PHD Filtering Tracking Algorithm with Unknown Measurement Noise Distribution","publication_year":2023,"publication_date":"2023-12-08","ids":{"openalex":"https://openalex.org/W4396629980","doi":"https://doi.org/10.1145/3654446.3654467"},"language":"en","primary_location":{"id":"doi:10.1145/3654446.3654467","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3654446.3654467","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3654446.3654467","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Signal Processing, Computer Networks and Communications","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3654446.3654467","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092276262","display_name":"Hui Zhang","orcid":"https://orcid.org/0009-0001-8391-7492"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Zhang","raw_affiliation_strings":["Dalian Maritime University, China"],"raw_orcid":"https://orcid.org/0009-0001-8391-7492","affiliations":[{"raw_affiliation_string":"Dalian Maritime University, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103067408","display_name":"Xiaoming Liu","orcid":"https://orcid.org/0009-0000-9334-5750"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoming Liu","raw_affiliation_strings":["Dalian Maritime University, China"],"raw_orcid":"https://orcid.org/0009-0000-9334-5750","affiliations":[{"raw_affiliation_string":"Dalian Maritime University, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029938642","display_name":"Yuqing Cheng","orcid":"https://orcid.org/0009-0004-4127-1840"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuqing Cheng","raw_affiliation_strings":["Dalian Maritime University, China"],"raw_orcid":"https://orcid.org/0009-0004-4127-1840","affiliations":[{"raw_affiliation_string":"Dalian Maritime University, China","institution_ids":["https://openalex.org/I43313876"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I43313876"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26441327,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"118","last_page":"123"},"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9718999862670898,"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/T11325","display_name":"Inertial Sensor and Navigation","score":0.9641000032424927,"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/algorithm","display_name":"Algorithm","score":0.698074460029602},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6440228819847107},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.6433754563331604},{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.5917246341705322},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.5300588011741638},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5210049152374268},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.5109683275222778},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5087442994117737},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5010266304016113},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4386095404624939},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.43178656697273254},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38608241081237793},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3725825548171997},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33250975608825684},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.2175796627998352},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1814914345741272},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.09786269068717957}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.698074460029602},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6440228819847107},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6433754563331604},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.5917246341705322},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.5300588011741638},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5210049152374268},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.5109683275222778},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5087442994117737},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5010266304016113},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4386095404624939},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.43178656697273254},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38608241081237793},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3725825548171997},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33250975608825684},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2175796627998352},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1814914345741272},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.09786269068717957},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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:10.1145/3654446.3654467","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3654446.3654467","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3654446.3654467","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Signal Processing, Computer Networks and Communications","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3654446.3654467","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3654446.3654467","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3654446.3654467","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Signal Processing, Computer Networks and Communications","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4396629980.pdf","grobid_xml":"https://content.openalex.org/works/W4396629980.grobid-xml"},"referenced_works_count":9,"referenced_works":["https://openalex.org/W2014787937","https://openalex.org/W2023428235","https://openalex.org/W2077969890","https://openalex.org/W2080777453","https://openalex.org/W2095376999","https://openalex.org/W2105905583","https://openalex.org/W4244635056","https://openalex.org/W4300724243","https://openalex.org/W6680059044"],"related_works":["https://openalex.org/W2130674020","https://openalex.org/W2093748878","https://openalex.org/W2333771223","https://openalex.org/W2120056845","https://openalex.org/W1981531423","https://openalex.org/W4394861761","https://openalex.org/W2035264131","https://openalex.org/W1679012645","https://openalex.org/W1925461966","https://openalex.org/W3036468168"],"abstract_inverted_index":{"In":[0],"practical":[1],"target":[2,42,83,137,146],"tracking":[3,23,179],"applications,":[4],"it":[5],"is":[6,30,156,168,175,181],"challenging":[7],"to":[8,127],"accurately":[9],"establish":[10],"the":[11,34,50,107,111,116,120,129,135,141,144,153,159,162,171,184],"measurement":[12,28,38,74,172],"noise":[13,29,39,68,173],"covariance":[14,69,174],"model.":[15],"The":[16,56],"traditional":[17,185],"Probability":[18,51,59,97],"Hypothesis":[19,52,60,98],"Density":[20,53,61,99],"filtering":[21,54,64,112,195],"algorithm's":[22],"performance":[24],"rapidly":[25],"deteriorates":[26],"when":[27],"unknown.":[31],"To":[32,86],"address":[33],"impact":[35],"of":[36,72,131,143,164],"unknown":[37,73,169],"on":[40,134],"multi-extended":[41],"tracking,":[43],"Variational":[44,57,95,190],"Bayesian":[45,58,96,191],"methods":[46],"are":[47],"introduced":[48],"into":[49],"algorithm.":[55,100],"algorithm":[62,78,102,155],"allows":[63],"calculations":[65],"by":[66],"estimating":[67],"in":[70,110,125],"cases":[71],"noise.":[75],"However,":[76],"this":[77,88,90],"still":[79],"suffers":[80],"from":[81],"inaccurate":[82],"number":[84,163],"estimation.":[85],"solve":[87],"problem,":[89],"paper":[91],"proposes":[92],"an":[93],"improved":[94],"This":[101],"introduces":[103],"elliptical":[104],"thresholds":[105,121],"after":[106],"prediction":[108],"step":[109],"process":[113],"and":[114,139,170,177,189],"utilizes":[115],"measurements":[117,133],"retained":[118],"within":[119],"for":[122,158],"further":[123],"calculation,":[124],"order":[126],"reduce":[128],"influence":[130],"clutter":[132],"extended":[136,145,166],"measurement,":[138],"improve":[140],"precision":[142],"state":[147],"clustering.":[148],"Experimental":[149],"results":[150],"show":[151],"that":[152],"proposed":[154],"suitable":[157],"situation":[160],"where":[161],"multiple":[165],"targets":[167],"unknown,":[176],"its":[178],"accuracy":[180],"better":[182],"than":[183],"probability":[186,192],"hypothesis":[187,193],"density":[188,194],"algorithms.":[196]},"counts_by_year":[],"updated_date":"2026-08-25T07:29:55.448023","created_date":"2025-10-10T00:00:00"}