{"id":"W2106343541","doi":"10.1109/tsp.2011.2157499","title":"DOA Estimation of Temporally and Spatially Correlated Narrowband Noncircular Sources in Spatially Correlated White Noise","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Narrowband; Subspace topology; White noise; Mathematics; Correlation; Cramér–Rao bound; Algorithm; Signal-to-noise ratio (imaging); Signal subspace; Signal processing; Estimation theory; Noise (video); Statistics; Computer science; Mathematical analysis; Telecommunications; 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":[0.0004275653,0.0002318714,0.0003425731,0.0006022328,0.0001474896,0.00007294759,0.0003346194,0.0001791613,0.0000404946],"category_scores_gemma":[0.00001956908,0.0002392311,0.00007124043,0.0009498117,0.000162458,0.00113198,0.000005528448,0.0002864646,0.00000407035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000556919,"about_ca_system_score_gemma":0.000277472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003366351,"about_ca_topic_score_gemma":0.00008299748,"domain_scores_codex":[0.9981201,0.0001092894,0.0007542094,0.0004046152,0.0004042471,0.0002075869],"domain_scores_gemma":[0.9987859,0.00009323427,0.0004899915,0.000246385,0.0002971901,0.00008733069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003674037,0.001076631,0.00319977,0.0006430969,0.00008607698,0.00003085645,0.01904552,0.4183802,0.03984989,0.0002029272,0.00001000718,0.5171076],"study_design_scores_gemma":[0.0005718064,0.0003020769,0.001953613,0.0006657062,0.00003559158,0.0000243058,0.00003870331,0.7788154,0.2163249,0.001023122,0.000001684953,0.0002430265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1488352,0.0000583475,0.8500001,0.00003405491,0.0001113474,0.0002886533,0.000004058337,0.0002300919,0.000438153],"genre_scores_gemma":[0.9194601,0.00001016631,0.08042727,0.00002562978,0.000004729336,0.00002363718,0.000002207576,0.00002092496,0.00002529935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.770625,"threshold_uncertainty_score":0.9755562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01899203473198949,"score_gpt":0.2380482756005158,"score_spread":0.2190562408685263,"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."}}