{"id":"https://openalex.org/W7138942483","doi":"https://doi.org/10.1109/access.2026.3675490","title":"UAV Classification Using Attentive Binarized CNN for Micro-Doppler Spectrograms","display_name":"UAV Classification Using Attentive Binarized CNN for Micro-Doppler Spectrograms","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7138942483","doi":"https://doi.org/10.1109/access.2026.3675490"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3675490","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3675490","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3675490","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075858816","display_name":"Ipsita Paul","orcid":null},"institutions":[{"id":"https://openalex.org/I67357951","display_name":"KIIT University","ror":"https://ror.org/00k8zt527","country_code":"IN","type":"education","lineage":["https://openalex.org/I67357951"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Ipsita Paul","raw_affiliation_strings":["School of Computer Science and Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5128094672","display_name":"Mainak Bandyopadhyay","orcid":null},"institutions":[{"id":"https://openalex.org/I67357951","display_name":"KIIT University","ror":"https://ror.org/00k8zt527","country_code":"IN","type":"education","lineage":["https://openalex.org/I67357951"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mainak Bandyopadhyay","raw_affiliation_strings":["School of Computer Science and Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5075858816"],"corresponding_institution_ids":["https://openalex.org/I67357951"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.88956273,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"43149","last_page":"43170"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9621000289916992,"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9621000289916992,"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/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.014399999752640724,"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/T10891","display_name":"Radar Systems and Signal Processing","score":0.006399999838322401,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.7505999803543091},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.695900022983551},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5728999972343445},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5425999760627747},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5202000141143799},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.492000013589859},{"id":"https://openalex.org/keywords/high-fidelity","display_name":"High fidelity","score":0.48539999127388},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46399998664855957},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.43369999527931213}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8330000042915344},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.7505999803543091},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7253999710083008},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.695900022983551},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5728999972343445},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5425999760627747},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5202000141143799},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.492000013589859},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.48539999127388},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46399998664855957},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.43369999527931213},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.4336000084877014},{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.4271000027656555},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41830000281333923},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.40860000252723694},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4041000008583069},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.3756999969482422},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.3734999895095825},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.36010000109672546},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3549000024795532},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.3537999987602234},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3522999882698059},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3492000102996826},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.329800009727478},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3174999952316284},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.27480000257492065},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.26260000467300415}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3675490","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3675490","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:afd5a6b931fc4722b6aab7a8ac600a9d","is_oa":true,"landing_page_url":"https://doaj.org/article/afd5a6b931fc4722b6aab7a8ac600a9d","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 43149-43170 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3675490","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3675490","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8491767048835754,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Radar-based":[0],"micro-Doppler":[1],"spectrograms":[2],"offer":[3],"a":[4,55,60,128],"robust":[5],"representation":[6],"for":[7,33,58,75,133],"classifying":[8],"aerial":[9],"objects,":[10],"particularly":[11],"small":[12],"Unmanned":[13],"Aerial":[14],"Vehicles":[15],"(UAVs),":[16],"in":[17,83,158],"scenarios":[18],"where":[19],"optical":[20],"sensing":[21,144],"is":[22,67],"limited":[23],"or":[24],"infeasible.":[25],"However,":[26],"deploying":[27],"deep":[28],"Convolutional":[29],"Neural":[30],"Networks":[31],"(CNNs)":[32],"this":[34,50],"task":[35],"on":[36,88,99,108,145],"edge":[37],"devices":[38],"remains":[39],"challenging":[40],"due":[41],"to":[42,127,142],"their":[43],"high":[44],"computational":[45],"and":[46,62,72,130,155,161],"memory":[47],"demands.":[48],"In":[49],"work,":[51],"we":[52],"have":[53,105],"proposed":[54,101],"hybrid":[56],"approach,":[57],"creating":[59],"compact":[61],"energy-efficient":[63],"neural":[64],"architecture.":[65,103],"This":[66,124,148],"done":[68],"by":[69,120],"integrating":[70],"binarization":[71],"attention":[73],"modules":[74],"enhancing":[76],"representational":[77],"capacity":[78],"along":[79],"with":[80,96],"significant":[81],"reduction":[82],"model":[84,153],"complexity.":[85],"Experimental":[86],"evaluations":[87],"the":[89,114,150],"DIAT-\u03bcSAT":[90],"dataset":[91,110],"demonstrate":[92],"good":[93],"classification":[94,156],"performance":[95],"97.83%":[97],"accuracy":[98],"our":[100],"QA_MVGG_10":[102],"We":[104],"further":[106,140],"validated":[107],"another":[109],"RRN_ATR_NET,":[111],"which":[112],"confirms":[113],"model\u2019s":[115],"robustness,":[116],"i.e.,":[117],"cross-dataset":[118],"generalization,":[119],"achieving":[121],"99.17%":[122],"accuracy.":[123],"work":[125],"contributes":[126],"scalable":[129],"interpretable":[131],"solution":[132],"on-device":[134],"GPU":[135],"inference":[136],"that":[137],"can":[138],"be":[139],"extended":[141],"radar":[143],"edge-deployable":[146],"systems.":[147],"bridges":[149],"gap":[151],"between":[152],"efficiency":[154],"fidelity":[157],"modern":[159],"defense":[160],"surveillance":[162],"applications.":[163]},"counts_by_year":[],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2026-03-20T00:00:00"}
