{"id":"https://openalex.org/W2082841700","doi":"https://doi.org/10.1109/sam.2012.6250481","title":"A heterogeneous Markov chain model of target existence variable for Bayesian track-before-detect","display_name":"A heterogeneous Markov chain model of target existence variable for Bayesian track-before-detect","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W2082841700","doi":"https://doi.org/10.1109/sam.2012.6250481","mag":"2082841700"},"language":"en","primary_location":{"id":"doi:10.1109/sam.2012.6250481","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sam.2012.6250481","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM)","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/A5074326682","display_name":"Shuxiang Xia","orcid":"https://orcid.org/0009-0008-7795-1361"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"S.Z. Xia","raw_affiliation_strings":["National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China","National Lab of Radar Signal Processing, Xidian University, Xi\u00bfan 710071, China"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"National Lab of Radar Signal Processing, Xidian University, Xi\u00bfan 710071, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036145098","display_name":"Fengzhou Dai","orcid":"https://orcid.org/0000-0003-2166-2516"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"F.Z. Dai","raw_affiliation_strings":["National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China","Nat. Lab. of Radar Signal Process., Xidian Univ., Xi'an, China#TAB#"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Nat. Lab. of Radar Signal Process., Xidian Univ., Xi'an, China#TAB#","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103349550","display_name":"H.W. Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"H.W. Liu","raw_affiliation_strings":["National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China","Nat. Lab. of Radar Signal Process., Xidian Univ., Xi'an, China#TAB#"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Nat. Lab. of Radar Signal Process., Xidian Univ., Xi'an, China#TAB#","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5074326682"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1166906,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"153","issue":null,"first_page":"253","last_page":"256"},"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":0.9532999992370605,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9449999928474426,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.745837390422821},{"id":"https://openalex.org/keywords/track-before-detect","display_name":"Track-before-detect","score":0.7104682326316833},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.652536153793335},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.6379013657569885},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6350217461585999},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.539550244808197},{"id":"https://openalex.org/keywords/track","display_name":"Track (disk drive)","score":0.48157691955566406},{"id":"https://openalex.org/keywords/homogeneous","display_name":"Homogeneous","score":0.4771706163883209},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.45705175399780273},{"id":"https://openalex.org/keywords/variable-order-bayesian-network","display_name":"Variable-order Bayesian network","score":0.433298259973526},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.428536057472229},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.42725831270217896},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.42519891262054443},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.42052987217903137},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3881549835205078},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.3318922221660614},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27227431535720825},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.25298553705215454},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17115601897239685},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.16450509428977966},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.12215283513069153}],"concepts":[{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.745837390422821},{"id":"https://openalex.org/C173028925","wikidata":"https://www.wikidata.org/wiki/Q7831461","display_name":"Track-before-detect","level":4,"score":0.7104682326316833},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.652536153793335},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.6379013657569885},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6350217461585999},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.539550244808197},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.48157691955566406},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.4771706163883209},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.45705175399780273},{"id":"https://openalex.org/C71983512","wikidata":"https://www.wikidata.org/wiki/Q7915687","display_name":"Variable-order Bayesian network","level":4,"score":0.433298259973526},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.428536057472229},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.42725831270217896},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.42519891262054443},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.42052987217903137},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3881549835205078},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3318922221660614},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27227431535720825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25298553705215454},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17115601897239685},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.16450509428977966},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.12215283513069153},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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:10.1109/sam.2012.6250481","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sam.2012.6250481","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM)","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":7,"referenced_works":["https://openalex.org/W1972802971","https://openalex.org/W2038791878","https://openalex.org/W2055936398","https://openalex.org/W2126736494","https://openalex.org/W2150441875","https://openalex.org/W2153016480","https://openalex.org/W2160337655"],"related_works":["https://openalex.org/W2082284720","https://openalex.org/W2116722627","https://openalex.org/W1510894296","https://openalex.org/W2134386692","https://openalex.org/W2194396582","https://openalex.org/W2799426416","https://openalex.org/W2048052024","https://openalex.org/W4385889111","https://openalex.org/W2079506490","https://openalex.org/W2088655882"],"abstract_inverted_index":{"A":[0],"heterogeneous":[1],"Markov":[2],"chain":[3],"(MC)":[4],"model,":[5,11],"instead":[6],"of":[7,34,43,46],"the":[8,32,41,44],"homogeneous":[9],"MC":[10],"is":[12,28],"proposed":[13,26],"in":[14],"this":[15],"paper":[16],"to":[17,40],"describe":[18],"target":[19,35],"existence":[20,36],"variable":[21],"for":[22],"Bayesian":[23],"track-before-detect.":[24],"The":[25],"model":[27],"more":[29],"consistent":[30],"with":[31],"transitions":[33],"variable,":[37],"which":[38],"leads":[39],"improvement":[42],"performance":[45],"detection":[47],"and":[48],"tracking.":[49]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}