{"id":"W3136957033","doi":"10.1109/lgrs.2021.3062373","title":"DOA Estimation for HFSWR Target Based on PSO-ELM","year":2021,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Azimuth; Extreme learning machine; Computer science; Radar; Direction of arrival; Algorithm; Particle swarm optimization; Artificial neural network; Support vector machine; Range (aeronautics); Backpropagation; Mean squared error; Pattern recognition (psychology); Artificial intelligence; Mathematics; Engineering; Telecommunications; Statistics; Antenna (radio)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003583123,0.0006128591,0.0006165407,0.0003478941,0.0002474638,0.0003991431,0.0004464283,0.0005887426,0.0009602198],"category_scores_gemma":[0.001118652,0.0002625838,0.0005522118,0.0004005315,0.0002137114,0.0005124962,0.0004571738,0.0005951234,0.0002340845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002105608,"about_ca_system_score_gemma":0.0004122436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003729035,"about_ca_topic_score_gemma":0.002366821,"domain_scores_codex":[0.999818,0.00003641984,0.00001389758,0.00004706946,0.0000663711,0.00001816741],"domain_scores_gemma":[0.9997706,0.00009595315,0.00003872738,0.00001638087,0.00006769489,0.00001071044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007463494,0.00004625755,0.002025258,0.00009204301,0.0000532949,0.00008221557,0.00008269524,0.8887472,0.007670247,0.002732007,0.001054483,0.09733966],"study_design_scores_gemma":[0.000004434282,0.000007932649,0.000193497,0.000001460156,0.000002141677,0.000006514835,0.000002781071,0.9992322,0.0002630829,0.0001774073,0.0001061438,0.000002299677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03223315,0.0002001673,0.9652664,0.0001041846,0.00003549181,0.00002346859,0.00003171789,0.0002564081,0.001848996],"genre_scores_gemma":[0.7367508,0.000358723,0.2590981,0.0000994191,0.00006549141,0.0001595818,0.0002351342,0.00005998166,0.003172764],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003729035,"threshold_uncertainty_score":0.007414699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01061309992969911,"score_gpt":0.2184507381849921,"score_spread":0.207837638255293,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}