{"id":"W2545819561","doi":"10.1109/acssc.2007.4487639","title":"Semi-Blind Adaptive Beamforming for Cyclostationary Signals: A Kalman Filtering Approach","year":2007,"lang":"en","type":"article","venue":"Conference record/Conference record - Asilomar Conference on Signals, Systems, & Computers","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Cyclostationary process; Beamforming; Kalman filter; Adaptive beamformer; Computer science; Extended Kalman filter; Algorithm; Autoencoder; Control theory (sociology); Mathematical optimization; Mathematics; Artificial intelligence; Telecommunications; Channel (broadcasting); Artificial neural network","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":[0.0005174251,0.0005680261,0.0004923677,0.0004384307,0.0003490833,0.0005785261,0.0005984663,0.0005743278,0.001340797],"category_scores_gemma":[0.00156919,0.0003592473,0.000462325,0.0004966595,0.0006432008,0.0009096729,0.0005222258,0.0008646375,0.0005314405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000569553,"about_ca_system_score_gemma":0.0009803375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002870865,"about_ca_topic_score_gemma":0.00361088,"domain_scores_codex":[0.9997211,0.00006711105,0.00002035211,0.00006555929,0.0001035998,0.00002225863],"domain_scores_gemma":[0.9994897,0.0002837392,0.00006336586,0.00004271448,0.0001083612,0.00001220205],"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":[0.00008147227,0.00004527161,0.0008419853,0.0001685412,0.00008258703,0.00006164606,0.0001362743,0.5943169,0.02359375,0.09280561,0.00227906,0.285587],"study_design_scores_gemma":[0.000008274569,0.00002982286,0.0001734558,0.00001225291,0.00001074126,0.00003467409,0.000008404161,0.9831784,0.003313619,0.01003466,0.003176959,0.0000188181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006760831,0.00006607782,0.9988614,0.00003540469,0.00001121373,0.000004486171,0.000006405841,0.00004365887,0.0002951855],"genre_scores_gemma":[0.1545099,0.001097719,0.8398429,0.000171708,0.0001540914,0.0001733905,0.0001364154,0.0000888912,0.003825034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002870865,"threshold_uncertainty_score":0.005708277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0937800095394437,"score_gpt":0.3137663560487998,"score_spread":0.2199863465093561,"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."}}