{"id":"https://openalex.org/W4379983741","doi":"https://doi.org/10.1109/lsp.2023.3284659","title":"Efficient TFI-Based Depth-Tunable LPI Radar Waveform Recognition Network","display_name":"Efficient TFI-Based Depth-Tunable LPI Radar Waveform Recognition Network","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4379983741","doi":"https://doi.org/10.1109/lsp.2023.3284659"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2023.3284659","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2023.3284659","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-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/A5037220121","display_name":"Xiti Wang","orcid":"https://orcid.org/0000-0002-5750-9380"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiti Wang","raw_affiliation_strings":["School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034583553","display_name":"Zhiyong Luo","orcid":"https://orcid.org/0000-0002-4084-4027"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyong Luo","raw_affiliation_strings":["School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen, China","Pengcheng Laboratory, Shenzhen, China","Shenzhen Key Laboratory of Navigation and Communication Integration, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"Pengcheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"Shenzhen Key Laboratory of Navigation and Communication Integration, Shenzhen, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5037220121"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":2.2666,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.90255294,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"30","issue":null,"first_page":"713","last_page":"717"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9980999827384949,"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9980999827384949,"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9975000023841858,"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/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7511454820632935},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.7331132888793945},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.6703886985778809},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6312605142593384},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.6199181079864502},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.5779480934143066},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4983952045440674},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41943663358688354},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37745237350463867},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.32292184233665466},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.27955031394958496},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.23733261227607727},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1062551736831665},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.07501477003097534}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7511454820632935},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.7331132888793945},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.6703886985778809},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6312605142593384},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.6199181079864502},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.5779480934143066},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4983952045440674},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41943663358688354},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37745237350463867},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.32292184233665466},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27955031394958496},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.23733261227607727},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1062551736831665},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.07501477003097534},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2023.3284659","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2023.3284659","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1594587862","https://openalex.org/W1985437849","https://openalex.org/W2194775991","https://openalex.org/W2776855315","https://openalex.org/W2781731889","https://openalex.org/W2947560542","https://openalex.org/W2987852271","https://openalex.org/W2999696229","https://openalex.org/W3127701791","https://openalex.org/W3158385995","https://openalex.org/W3187153100","https://openalex.org/W3214421355","https://openalex.org/W4205751617","https://openalex.org/W4220657966","https://openalex.org/W4237091046","https://openalex.org/W4308085475","https://openalex.org/W4312456708","https://openalex.org/W6635560335","https://openalex.org/W6683965311","https://openalex.org/W6738491991","https://openalex.org/W6747043858"],"related_works":["https://openalex.org/W1974895211","https://openalex.org/W2129841057","https://openalex.org/W3040712279","https://openalex.org/W2176409448","https://openalex.org/W2364769705","https://openalex.org/W2056136368","https://openalex.org/W2374664672","https://openalex.org/W4367555392","https://openalex.org/W2538520412","https://openalex.org/W2883092465"],"abstract_inverted_index":{"Low":[0],"probability":[1],"of":[2,60,128],"intercept":[3],"(LPI)":[4],"radar":[5,111],"waveform":[6,112],"recognition":[7,17,46,86,135],"(LWR)":[8],"based":[9],"on":[10],"deep":[11],"learning":[12],"can":[13],"significantly":[14],"enhance":[15,44],"the":[16,25,45,50,55,80,85,116,119,129,134],"accuracy.":[18,136],"However,":[19],"most":[20],"existing":[21],"LWR":[22,62],"networks":[23,131],"overlook":[24],"algorithmic":[26],"complexity":[27,52,82,121],"and":[28,33,57,83,100],"disparities":[29],"between":[30],"visual":[31],"images":[32,35],"time-frequency":[34],"(TFIs).":[36],"In":[37],"practical":[38],"applications,":[39],"it":[40],"is":[41],"critical":[42],"to":[43,53],"accuracy":[47,87],"while":[48,132],"minimizing":[49],"computational":[51,81,120],"satisfy":[54],"reliability":[56],"timeliness":[58],"requirements":[59],"an":[61,70],"network.":[63],"To":[64],"address":[65],"this":[66],"issue,":[67],"we":[68],"propose":[69],"efficient":[71],"depth-tunable":[72],"network":[73,92],"(EDTN)":[74],"for":[75,98],"LWR.":[76],"The":[77],"EDTN":[78,117],"reduces":[79,118],"enhances":[84],"by":[88,122],"adopting":[89],"a":[90,108],"flexible":[91],"structure,":[93],"using":[94],"convolution":[95],"methods":[96],"appropriate":[97],"TFIs,":[99],"combining":[101],"several":[102],"beneficial":[103],"designs.":[104],"Our":[105],"experiments":[106],"with":[107],"classical":[109],"LPI":[110],"dataset":[113],"demonstrate":[114],"that":[115,127],"more":[123],"than":[124],"95%":[125],"over":[126],"state-of-the-art":[130],"maintaining":[133]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":4}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}