{"id":"https://openalex.org/W4409156990","doi":"https://doi.org/10.1109/ieeeconf60004.2024.10942826","title":"Sequential Reinforcement Learning-Based Waveform Adaptation for Dynamic Decision-Making in Tracking","display_name":"Sequential Reinforcement Learning-Based Waveform Adaptation for Dynamic Decision-Making in Tracking","publication_year":2024,"publication_date":"2024-10-27","ids":{"openalex":"https://openalex.org/W4409156990","doi":"https://doi.org/10.1109/ieeeconf60004.2024.10942826"},"language":"en","primary_location":{"id":"doi:10.1109/ieeeconf60004.2024.10942826","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf60004.2024.10942826","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 58th Asilomar Conference on Signals, Systems, and Computers","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/A5103237888","display_name":"A. Brent Eastman","orcid":"https://orcid.org/0009-0005-8716-1628"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abigail Eastman","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University,Tempe,AZ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University,Tempe,AZ","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069246416","display_name":"Antonia Papandreou\u2010Suppappola","orcid":"https://orcid.org/0000-0002-5223-6256"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Antonia Papandreou-Suppappola","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University,Tempe,AZ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University,Tempe,AZ","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1861","last_page":"1865"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14225","display_name":"Advanced Sensor and Control Systems","score":0.6802999973297119,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14225","display_name":"Advanced Sensor and Control Systems","score":0.6802999973297119,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/reinforcement-learning","display_name":"Reinforcement learning","score":0.8956859111785889},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7322041392326355},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.6425904035568237},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.6332151293754578},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5933808088302612},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47768789529800415},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.46446388959884644},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.41605687141418457},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13227277994155884},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.06631302833557129}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8956859111785889},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7322041392326355},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.6425904035568237},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6332151293754578},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5933808088302612},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47768789529800415},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.46446388959884644},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.41605687141418457},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13227277994155884},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.06631302833557129},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"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/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","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},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ieeeconf60004.2024.10942826","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf60004.2024.10942826","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 58th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6299999952316284,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G5008084248","display_name":null,"funder_award_id":"W911NF-23-2-0225","funder_id":"https://openalex.org/F4320338295","funder_display_name":"Army Research Laboratory"},{"id":"https://openalex.org/G7337180856","display_name":null,"funder_award_id":"FA9550-23-1-0328","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"}],"funders":[{"id":"https://openalex.org/F4320338279","display_name":"Air Force Office of Scientific Research","ror":"https://ror.org/011e9bt93"},{"id":"https://openalex.org/F4320338295","display_name":"Army Research Laboratory","ror":"https://ror.org/011hc8f90"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2137632745","https://openalex.org/W2160337655","https://openalex.org/W2532143293","https://openalex.org/W2799091251","https://openalex.org/W2939549350","https://openalex.org/W2993197237","https://openalex.org/W3175434624","https://openalex.org/W4206254735","https://openalex.org/W4214717370","https://openalex.org/W4285105923","https://openalex.org/W4301711235","https://openalex.org/W4381745300","https://openalex.org/W4387802130","https://openalex.org/W4390776944","https://openalex.org/W4401164068"],"related_works":["https://openalex.org/W4310083477","https://openalex.org/W2328553770","https://openalex.org/W2920061524","https://openalex.org/W1974895211","https://openalex.org/W2129841057","https://openalex.org/W3040712279","https://openalex.org/W2176409448","https://openalex.org/W2364769705","https://openalex.org/W1977959518","https://openalex.org/W2056136368"],"abstract_inverted_index":{"This":[0,52],"paper":[1],"considers":[2],"a":[3,9,38,54,102],"waveform":[4,75],"design":[5],"problem":[6],"for":[7],"tracking":[8,49,113],"target":[10],"in":[11,27],"an":[12,44],"unknown":[13],"and":[14,76,108],"time-varying":[15],"environment.":[16],"The":[17],"varying":[18],"conditions":[19,50],"are":[20,81],"due":[21],"to":[22],"potentially":[23],"high":[24],"interference":[25],"levels":[26],"different":[28],"frequency":[29,79],"sub-bands":[30],"as":[31],"the":[32,73,77,98,111],"radar":[33],"is":[34,53],"sharing":[35],"spectrum":[36],"with":[37,101],"wireless":[39],"communication":[40],"system.":[41],"We":[42,96],"propose":[43],"approach":[45],"that":[46,58],"sequentially":[47],"improves":[48],"using$Q$learning.":[51],"reinforcement":[55],"learning":[56],"method":[57],"does":[59],"not":[60],"require":[61],"prior":[62],"knowledge":[63],"or":[64],"training":[65],"under":[66],"dynamically-varying":[67],"conditions.":[68],"In":[69],"our":[70],"formulation,":[71],"both":[72],"transmit":[74],"transmission":[78],"sub-band":[80],"selected":[82],"at":[83],"each":[84],"time":[85],"step":[86],"based":[87],"on":[88],"suitably":[89],"designed":[90,94],"cost":[91],"functions":[92],"appropriately":[93],"costs.":[95],"integrate":[97],"Q-learning":[99],"algorithm":[100],"sequential":[103],"Monte":[104],"Carlo":[105],"filtering":[106],"method,":[107],"we":[109],"demonstrate":[110],"improved":[112],"performance":[114],"using":[115],"simulations.":[116]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}