{"id":"https://openalex.org/W4388040503","doi":"https://doi.org/10.1109/pimrc56721.2023.10293804","title":"Resource Allocation for Multi-target Radar Tracking via Constrained Deep Reinforcement Learning","display_name":"Resource Allocation for Multi-target Radar Tracking via Constrained Deep Reinforcement Learning","publication_year":2023,"publication_date":"2023-09-05","ids":{"openalex":"https://openalex.org/W4388040503","doi":"https://doi.org/10.1109/pimrc56721.2023.10293804"},"language":"en","primary_location":{"id":"doi:10.1109/pimrc56721.2023.10293804","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/pimrc56721.2023.10293804","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101440360","display_name":"Ziyang Lu","orcid":"https://orcid.org/0000-0001-6586-3737"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziyang Lu","raw_affiliation_strings":["Syracuse University,Department of Electrical Engineering and Computer Science,Syracuse,NY","Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University,Department of Electrical Engineering and Computer Science,Syracuse,NY","institution_ids":["https://openalex.org/I70983195"]},{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076753154","display_name":"M. Cenk Gursoy","orcid":"https://orcid.org/0000-0002-7352-1013"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"M. Cenk Gursoy","raw_affiliation_strings":["Syracuse University,Department of Electrical Engineering and Computer Science,Syracuse,NY","Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University,Department of Electrical Engineering and Computer Science,Syracuse,NY","institution_ids":["https://openalex.org/I70983195"]},{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY","institution_ids":["https://openalex.org/I70983195"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I70983195"],"apc_list":null,"apc_paid":null,"fwci":0.4356,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.60578697,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"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":0.9994999766349792,"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":0.9994999766349792,"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/T10891","display_name":"Radar Systems and Signal Processing","score":0.9979000091552734,"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"}},{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9975000023841858,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8270764350891113},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8246047496795654},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.600188136100769},{"id":"https://openalex.org/keywords/offline-learning","display_name":"Offline learning","score":0.5919409394264221},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5223468542098999},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5103175044059753},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.508293092250824},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.49725010991096497},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4743950366973877},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.4684339463710785},{"id":"https://openalex.org/keywords/resource-allocation","display_name":"Resource allocation","score":0.4114833474159241},{"id":"https://openalex.org/keywords/online-learning","display_name":"Online learning","score":0.36163002252578735},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08842960000038147}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8270764350891113},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8246047496795654},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.600188136100769},{"id":"https://openalex.org/C2780490138","wikidata":"https://www.wikidata.org/wiki/Q7079636","display_name":"Offline learning","level":3,"score":0.5919409394264221},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5223468542098999},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5103175044059753},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.508293092250824},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.49725010991096497},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4743950366973877},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4684339463710785},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.4114833474159241},{"id":"https://openalex.org/C2986087404","wikidata":"https://www.wikidata.org/wiki/Q15946010","display_name":"Online learning","level":2,"score":0.36163002252578735},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08842960000038147},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","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/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/pimrc56721.2023.10293804","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/pimrc56721.2023.10293804","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","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":25,"referenced_works":["https://openalex.org/W1518931405","https://openalex.org/W1771410628","https://openalex.org/W2033181556","https://openalex.org/W2063146632","https://openalex.org/W2145339207","https://openalex.org/W2145938889","https://openalex.org/W2170492303","https://openalex.org/W2804791273","https://openalex.org/W2883094398","https://openalex.org/W2962734844","https://openalex.org/W2963293747","https://openalex.org/W3002224236","https://openalex.org/W3004263011","https://openalex.org/W3017347546","https://openalex.org/W3036812998","https://openalex.org/W3102027520","https://openalex.org/W3104100176","https://openalex.org/W3157999805","https://openalex.org/W3193491369","https://openalex.org/W4243362240","https://openalex.org/W6638018090","https://openalex.org/W6681158678","https://openalex.org/W6751725685","https://openalex.org/W6760822370","https://openalex.org/W6773211229"],"related_works":["https://openalex.org/W4225619808","https://openalex.org/W4400438570","https://openalex.org/W4376223516","https://openalex.org/W4226042081","https://openalex.org/W4388926065","https://openalex.org/W4308935744","https://openalex.org/W4362599051","https://openalex.org/W4386715814","https://openalex.org/W2952181314","https://openalex.org/W3207447243"],"abstract_inverted_index":{"In":[0,24],"this":[1,92],"work,":[2],"we":[3],"propose":[4],"a":[5,21,59,69,80,106],"constrained":[6],"deep":[7,34,63],"reinforcement":[8,64],"learning":[9,65,142],"(CDRL)":[10],"based":[11,110],"approach":[12],"to":[13,85,139],"address":[14,91],"resource":[15],"allocation":[16],"for":[17],"multi-target":[18],"tracking":[19,82],"in":[20,61,79,100,105,152],"radar":[22],"system.":[23],"the":[25,30,33,38,62,86,102,112,119,141,145,150,153,157],"proposed":[26,45],"CDRL":[27,46,54,98,127,130,134],"algorithm,":[28],"both":[29,125],"parameters":[31],"of":[32,49,72,88,118],"Q-network":[35],"(DQN)":[36],"and":[37,55,115,128,144,155],"dual":[39],"variable":[40],"are":[41,131],"learned":[42],"simultaneously.":[43],"The":[44],"framework":[47],"consists":[48],"two":[50],"components,":[51],"namely":[52],"online":[53,129,146],"offline":[56,97,126],"CDRL.":[57],"Training":[58],"DQN":[60],"algorithm":[66,103],"usually":[67],"requires":[68],"large":[70],"amount":[71],"data,":[73],"which":[74,101],"may":[75],"not":[76],"be":[77],"available":[78],"target":[81],"task":[83],"due":[84],"scarcity":[87],"measurements.":[89],"We":[90],"challenge":[93],"by":[94],"proposing":[95],"an":[96],"framework,":[99],"evolves":[104],"virtual":[107],"environment":[108,154],"generated":[109],"on":[111],"current":[113],"observations":[114],"prior":[116],"knowledge":[117],"environment.":[120],"Simulation":[121],"results":[122],"show":[123],"that":[124],"critical.":[132],"Offline":[133],"provides":[135],"more":[136],"training":[137],"data":[138],"stabilize":[140],"process":[143],"component":[147],"can":[148],"sense":[149],"change":[151],"make":[156],"corresponding":[158],"adaptation.":[159]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}