{"id":"W4311238878","doi":"10.18280/mmep.090534","title":"Off-Grid Based DOA Estimation Algorithm Using Auto-Regression (1) Sparse Bayesian Learning with Linear Interpolation Model","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Algorithm; Grid; Computer science; Interpolation (computer graphics); Bayesian inference; Bayesian probability; Hyperparameter optimization; Pattern recognition (psychology); Mathematical optimization; Mathematics; Artificial intelligence; Support vector machine; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007302563,0.0004853976,0.0009633725,0.0005330881,0.0003653681,0.000704736,0.001345099,0.0008321072,0.002029105],"category_scores_gemma":[0.002142018,0.0004236515,0.0006240416,0.0007715255,0.0004187993,0.0008753572,0.001064708,0.001120933,0.001045435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004122527,"about_ca_system_score_gemma":0.001104188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005257878,"about_ca_topic_score_gemma":0.005660963,"domain_scores_codex":[0.9994736,0.0001140899,0.0000331761,0.0001458612,0.000185496,0.0000477724],"domain_scores_gemma":[0.9993569,0.0002482622,0.00008618684,0.00009289515,0.0001911604,0.00002456233],"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":[0.0001159381,0.00008383,0.001737218,0.0001120307,0.00006543323,0.00006848019,0.0001308815,0.4924669,0.008839087,0.01398463,0.002320867,0.4800746],"study_design_scores_gemma":[0.000007633672,0.00001829142,0.0001844615,0.000004258169,0.000004715103,0.00003289036,0.00000611736,0.9962537,0.00104717,0.001689805,0.0007445476,0.000006436637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003258148,0.00006155441,0.9956864,0.00004252959,0.00001056753,0.00001455704,0.00001802973,0.0002597448,0.0006484856],"genre_scores_gemma":[0.2197399,0.0002651113,0.7754964,0.0001631157,0.00004812567,0.0001580786,0.0002995544,0.0001336822,0.003696149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005257878,"threshold_uncertainty_score":0.01045454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02390049922315263,"score_gpt":0.2403208663534427,"score_spread":0.21642036713029,"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."}}