{"id":"https://openalex.org/W4372262622","doi":"https://doi.org/10.1109/icassp49357.2023.10096969","title":"Online Learning-Based Waveform Selection for Improved Vehicle Recognition in Automotive Radar","display_name":"Online Learning-Based Waveform Selection for Improved Vehicle Recognition in Automotive Radar","publication_year":2023,"publication_date":"2023-05-05","ids":{"openalex":"https://openalex.org/W4372262622","doi":"https://doi.org/10.1109/icassp49357.2023.10096969"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49357.2023.10096969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49357.2023.10096969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5033205515","display_name":"Charles E. Thornton","orcid":"https://orcid.org/0000-0002-2078-6472"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Charles E. Thornton","raw_affiliation_strings":["Wireless @ Virginia Tech,Bradley Department of ECE,Blacksburg,VA","Bradley Department of ECE, Wireless @ Virginia Tech, Blacksburg, VA"],"affiliations":[{"raw_affiliation_string":"Wireless @ Virginia Tech,Bradley Department of ECE,Blacksburg,VA","institution_ids":["https://openalex.org/I859038795"]},{"raw_affiliation_string":"Bradley Department of ECE, Wireless @ Virginia Tech, Blacksburg, VA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033761650","display_name":"William W. Howard","orcid":"https://orcid.org/0000-0001-7958-3218"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"William W. Howard","raw_affiliation_strings":["Wireless @ Virginia Tech,Bradley Department of ECE,Blacksburg,VA","Bradley Department of ECE, Wireless @ Virginia Tech, Blacksburg, VA"],"affiliations":[{"raw_affiliation_string":"Wireless @ Virginia Tech,Bradley Department of ECE,Blacksburg,VA","institution_ids":["https://openalex.org/I859038795"]},{"raw_affiliation_string":"Bradley Department of ECE, Wireless @ Virginia Tech, Blacksburg, VA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052487611","display_name":"R. Michael Buehrer","orcid":"https://orcid.org/0000-0002-7196-1154"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"R. Michael Buehrer","raw_affiliation_strings":["Wireless @ Virginia Tech,Bradley Department of ECE,Blacksburg,VA","Bradley Department of ECE, Wireless @ Virginia Tech, Blacksburg, VA"],"affiliations":[{"raw_affiliation_string":"Wireless @ Virginia Tech,Bradley Department of ECE,Blacksburg,VA","institution_ids":["https://openalex.org/I859038795"]},{"raw_affiliation_string":"Bradley Department of ECE, Wireless @ Virginia Tech, Blacksburg, VA","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5033205515"],"corresponding_institution_ids":["https://openalex.org/I859038795"],"apc_list":null,"apc_paid":null,"fwci":0.8824,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.79715352,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10891","display_name":"Radar Systems and Signal Processing","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T10891","display_name":"Radar Systems and Signal Processing","score":0.9994000196456909,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9973999857902527,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/waveform","display_name":"Waveform","score":0.786494255065918},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7383055090904236},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.6930721998214722},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5211198925971985},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5044676065444946},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4832904636859894},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.429848849773407},{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.41401636600494385},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.32111915946006775},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.22913679480552673},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.15382987260818481}],"concepts":[{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.786494255065918},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7383055090904236},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.6930721998214722},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5211198925971985},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5044676065444946},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4832904636859894},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.429848849773407},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.41401636600494385},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.32111915946006775},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.22913679480552673},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.15382987260818481},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49357.2023.10096969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49357.2023.10096969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":26,"referenced_works":["https://openalex.org/W1673310716","https://openalex.org/W2134939064","https://openalex.org/W2150560561","https://openalex.org/W2443717701","https://openalex.org/W2592680288","https://openalex.org/W2752599163","https://openalex.org/W2791950347","https://openalex.org/W2883596242","https://openalex.org/W2972356219","https://openalex.org/W2973885869","https://openalex.org/W2974922121","https://openalex.org/W2999001270","https://openalex.org/W2999016724","https://openalex.org/W3034831589","https://openalex.org/W3036812998","https://openalex.org/W3045897655","https://openalex.org/W3084655389","https://openalex.org/W3104100176","https://openalex.org/W3118293846","https://openalex.org/W3135569648","https://openalex.org/W3196978066","https://openalex.org/W4287388162","https://openalex.org/W4300589654","https://openalex.org/W6637131181","https://openalex.org/W6789956949","https://openalex.org/W6844928237"],"related_works":["https://openalex.org/W1974895211","https://openalex.org/W2129841057","https://openalex.org/W2176409448","https://openalex.org/W3040712279","https://openalex.org/W2364769705","https://openalex.org/W2056136368","https://openalex.org/W2374664672","https://openalex.org/W4367555392","https://openalex.org/W2883092465","https://openalex.org/W2114441484"],"abstract_inverted_index":{"This":[0],"paper":[1],"describes":[2],"important":[3],"considerations":[4],"and":[5,88],"challenges":[6],"associated":[7],"with":[8],"online":[9],"reinforcement-learning":[10],"based":[11,32],"waveform":[12,41,55],"selection":[13,56],"for":[14,85,93],"target":[15],"identification":[16],"in":[17,63,96],"frequency":[18],"modulated":[19],"continuous":[20],"wave":[21],"(FMCW)":[22],"automotive":[23],"radar":[24,67,78],"systems.":[25],"We":[26,48],"present":[27],"a":[28,40,71,83,89],"novel":[29],"learning":[30],"approach":[31],"on":[33],"satisficing":[34],"Thompson":[35],"sampling,":[36],"which":[37],"quickly":[38,60],"identifies":[39],"expected":[42,104],"to":[43,80],"yield":[44],"satisfactory":[45],"classification":[46,105],"performance.":[47],"demonstrate":[49],"through":[50],"measurement-level":[51],"simulations":[52],"that":[53],"effective":[54],"strategies":[57],"can":[58],"be":[59],"learned,":[61],"even":[62],"cases":[64],"where":[65],"the":[66,97],"must":[68],"select":[69,82],"from":[70],"large":[72],"catalog":[73],"of":[74,99],"candidate":[75],"waveforms.":[76],"The":[77],"learns":[79],"adaptively":[81],"bandwidth":[84],"appropriate":[86],"resolution":[87],"slow-time":[90],"unimodular":[91],"code":[92],"interference":[94],"mitigation":[95],"scene":[98],"interest":[100],"by":[101],"optimizing":[102],"an":[103],"metric.":[106]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}