{"id":"W2104845454","doi":"10.1109/wcnc.2011.5779403","title":"DOA estimation from temporally and spatially correlated narrowband signals with noncircular sources","year":2011,"lang":"en","type":"article","venue":"","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Narrowband; Subspace topology; Correlation; Variance (accounting); Signal subspace; Algorithm; SIGNAL (programming language); Estimation theory; Expression (computer science); Computer science; Signal-to-noise ratio (imaging); Mathematics; Random variable; Signal processing; Spatial correlation; Cramér–Rao bound; Stochastic process; Statistics; Noise (video); Artificial intelligence; Telecommunications","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":[0.0006074038,0.0006713507,0.0006230509,0.000763652,0.0002522663,0.0006946447,0.0005722184,0.0005513644,0.0006663206],"category_scores_gemma":[0.003607169,0.000388537,0.0005484081,0.0007352508,0.00039182,0.0007930422,0.000553387,0.0006319974,0.0003454129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002677806,"about_ca_system_score_gemma":0.0008034816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001351947,"about_ca_topic_score_gemma":0.00312418,"domain_scores_codex":[0.9995669,0.0001069567,0.00002594067,0.00009496254,0.0001792021,0.00002606118],"domain_scores_gemma":[0.9990206,0.0005167868,0.0002004017,0.0001093613,0.0001286999,0.00002404537],"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":[0.0002946392,0.00009062942,0.004802843,0.0003666381,0.0002885721,0.0003025254,0.000264253,0.4700762,0.09370586,0.02410528,0.001022503,0.40468],"study_design_scores_gemma":[0.00002071945,0.00007777961,0.002550859,0.00002430506,0.00005008656,0.0003367712,0.00004273322,0.9681732,0.01989997,0.006209973,0.002568831,0.00004486328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01373941,0.0002145039,0.9855532,0.00002777174,0.00001552073,0.000008879693,0.00002399878,0.00007418656,0.0003426358],"genre_scores_gemma":[0.2520052,0.0006727227,0.7451737,0.00005520343,0.00008713373,0.00005733667,0.0003190982,0.00006539273,0.001564252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001351947,"threshold_uncertainty_score":0.003212273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01700034886097278,"score_gpt":0.2158981580101695,"score_spread":0.1988978091491967,"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."}}