{"id":"W2025396508","doi":"10.1049/el.2010.2498","title":"Adaptive beamforming with joint robustness against covariance matrix uncertainty and signal steering vector mismatch","year":2010,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Adaptive beamformer; Covariance matrix; Robustness (evolution); Control theory (sociology); Diagonal; Beamforming; Covariance; Algorithm; Mathematics; Diagonal matrix; Computer science; Estimation of covariance matrices; Matrix (chemical analysis); Mathematical optimization; Statistics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003178003,0.0001840163,0.0002173047,0.000125657,0.0001254196,0.0001081049,0.0003814819,0.00006189493,0.000004007964],"category_scores_gemma":[0.0000214646,0.000170956,0.00003875787,0.000342919,0.00009394658,0.0005346606,0.00009153174,0.0004101295,0.000001259647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001099287,"about_ca_system_score_gemma":0.000141761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004255303,"about_ca_topic_score_gemma":0.00004786768,"domain_scores_codex":[0.9987202,0.00003296339,0.0002414403,0.0003532518,0.0002831974,0.0003689944],"domain_scores_gemma":[0.9992203,0.00007906657,0.0001947107,0.0003336296,0.00009802442,0.00007427857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005934427,0.00006315745,0.0001116354,0.00008037454,0.0000935733,0.00001984844,0.0008255041,0.1221077,0.7884426,0.05575112,0.0002784383,0.03216672],"study_design_scores_gemma":[0.000481014,0.0002841877,0.0002968484,0.0001095672,0.00001619728,0.00007787014,0.00002339929,0.7404056,0.2565414,0.0005646006,0.0007336113,0.0004657124],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2879071,0.00004067841,0.7105463,0.0009555885,0.00008849833,0.0001774147,0.000001367297,0.0002119385,0.00007117944],"genre_scores_gemma":[0.7258908,0.000008382646,0.2737807,0.0002302757,0.00003318094,0.00002645981,0.000001845322,0.00001589795,0.00001244025],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6182979,"threshold_uncertainty_score":0.6971382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007254560196589095,"score_gpt":0.2140926222556181,"score_spread":0.206838062059029,"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."}}