{"id":"https://openalex.org/W7131114978","doi":"https://doi.org/10.1109/jiot.2026.3667218","title":"Direction Prediction Error-Prioritized Experience Replay (DPE-PER) for Adversarial UAV Interception Under Partial Observability","display_name":"Direction Prediction Error-Prioritized Experience Replay (DPE-PER) for Adversarial UAV Interception Under Partial Observability","publication_year":2026,"publication_date":"2026-02-23","ids":{"openalex":"https://openalex.org/W7131114978","doi":"https://doi.org/10.1109/jiot.2026.3667218"},"language":null,"primary_location":{"id":"doi:10.1109/jiot.2026.3667218","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3667218","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","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/A5073509796","display_name":"Issaree Srisomboon","orcid":"https://orcid.org/0000-0003-1740-6608"},"institutions":[{"id":"https://openalex.org/I110273157","display_name":"Kookmin University","ror":"https://ror.org/0049erg63","country_code":"KR","type":"education","lineage":["https://openalex.org/I110273157"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Issaree Srisomboon","raw_affiliation_strings":["Department of Computer Science, Kookmin University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-1740-6608","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Kookmin University, Seoul, South Korea","institution_ids":["https://openalex.org/I110273157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067217362","display_name":"Sanghwan Lee","orcid":"https://orcid.org/0000-0001-8431-0097"},"institutions":[{"id":"https://openalex.org/I110273157","display_name":"Kookmin University","ror":"https://ror.org/0049erg63","country_code":"KR","type":"education","lineage":["https://openalex.org/I110273157"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sanghwan Lee","raw_affiliation_strings":["Department of Computer Science, Kookmin University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-8431-0097","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Kookmin University, Seoul, South Korea","institution_ids":["https://openalex.org/I110273157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I110273157"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29068736,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"10","first_page":"21938","last_page":"21951"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11133","display_name":"UAV Applications and Optimization","score":0.2379000037908554,"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/T11133","display_name":"UAV Applications and Optimization","score":0.2379000037908554,"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/T12676","display_name":"Machine Learning and ELM","score":0.08630000054836273,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.055799998342990875,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/observability","display_name":"Observability","score":0.7688000202178955},{"id":"https://openalex.org/keywords/interception","display_name":"Interception","score":0.6521999835968018},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.649399995803833},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.47999998927116394},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.445499986410141},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4440000057220459},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.4325999915599823},{"id":"https://openalex.org/keywords/partially-observable-markov-decision-process","display_name":"Partially observable Markov decision process","score":0.42649999260902405},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.4120999872684479}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8513000011444092},{"id":"https://openalex.org/C36299963","wikidata":"https://www.wikidata.org/wiki/Q1369844","display_name":"Observability","level":2,"score":0.7688000202178955},{"id":"https://openalex.org/C61270487","wikidata":"https://www.wikidata.org/wiki/Q476843","display_name":"Interception","level":2,"score":0.6521999835968018},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.649399995803833},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5026999711990356},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.47999998927116394},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.445499986410141},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4440000057220459},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.4325999915599823},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.42649999260902405},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.4120999872684479},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.36739999055862427},{"id":"https://openalex.org/C145424490","wikidata":"https://www.wikidata.org/wiki/Q618465","display_name":"Remotely operated underwater vehicle","level":4,"score":0.3643999993801117},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.36320000886917114},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.35190001130104065},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3479999899864197},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3262999951839447},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.31859999895095825},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.298799991607666},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2888999879360199},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C74222875","wikidata":"https://www.wikidata.org/wiki/Q16000312","display_name":"Robot kinematics","level":4,"score":0.27810001373291016},{"id":"https://openalex.org/C79487989","wikidata":"https://www.wikidata.org/wiki/Q934680","display_name":"Vehicle dynamics","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2655999958515167}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2026.3667218","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3667218","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5095227956771851,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Autonomous":[0],"interception":[1,129],"of":[2,40,152],"unmanned":[3],"aerial":[4],"vehicles":[5],"(UAVs)":[6],"is":[7],"a":[8,46,56,63,124,132],"critical":[9],"capability":[10],"in":[11,127,135,163],"modern":[12],"defense,":[13],"requiring":[14],"agents":[15],"to":[16,83,104,139],"navigate":[17],"adversarial":[18,115,164],"maneuvers":[19],"under":[20],"environmental":[21],"uncertainty.":[22,94],"While":[23],"conventional":[24],"path":[25],"planning":[26],"often":[27],"assumes":[28],"full":[29],"observability":[30],"and":[31,92,131,144],"static":[32],"behaviors,":[33],"this":[34],"study":[35],"addresses":[36],"the":[37,99,112,150],"complex":[38],"challenge":[39],"intercepting":[41],"an":[42,79],"evasive":[43],"target":[44,106,128],"within":[45],"partially":[47],"observable":[48],"Markov":[49],"decision":[50],"process":[51],"(POMDP)":[52],"framework.":[53],"We":[54],"propose":[55],"deep":[57],"Q-network":[58],"(DQN)":[59],"framework":[60],"enhanced":[61],"by":[62],"novel":[64],"sampling":[65],"strategy:":[66],"direction":[67,88],"prediction":[68,81,89],"error\u2013prioritized":[69],"experience":[70,141,146,157],"replay":[71,75,142,147],"(DPE-PER).":[72],"Unlike":[73],"standard":[74,140],"mechanisms,":[76],"DPE-PER":[77,108],"utilizes":[78],"auxiliary":[80],"task":[82],"prioritize":[84],"transitions":[85],"based":[86],"on":[87,111],"(DP)":[90],"error":[91],"directional":[93],"By":[95],"emphasizing":[96],"experiences":[97],"where":[98],"agent\u2019s":[100],"internal":[101],"model":[102],"fails":[103],"anticipate":[105],"maneuvers,":[107],"focuses":[109],"learning":[110],"most":[113],"informative":[114],"interactions.":[116],"Extensive":[117],"simulations":[118],"demonstrate":[119],"that":[120],"our":[121],"approach":[122],"achieves":[123],"48.2%":[125],"improvement":[126],"rates":[130],"25.4%":[133],"increase":[134],"average":[136],"return":[137],"compared":[138],"(ER)":[143],"prioritized":[145],"(PER),":[148],"highlighting":[149],"effectiveness":[151],"integrating":[153],"predictive":[154],"signals":[155],"into":[156],"selection":[158],"for":[159],"robust":[160],"UAV":[161],"autonomy":[162],"environments.":[165]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-02-24T00:00:00"}