{"id":"W2079475265","doi":"10.1109/vetecs.2012.6240336","title":"Stochastic NDA CRLB for DOA Estimation over SIMO Systems","year":2012,"lang":"en","type":"article","venue":"","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Cramér–Rao bound; Distortion (music); Additive white Gaussian noise; Algorithm; Direction of arrival; Constant (computer programming); Channel (broadcasting); Gaussian; Mathematics; Circular buffer; Gaussian noise; Signal-to-noise ratio (imaging); Computer science; Upper and lower bounds; Complex normal distribution; Estimation theory; Telecommunications; Physics; Mathematical analysis; Bandwidth (computing)","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":[0.002574695,0.001535275,0.001002418,0.001781807,0.00051661,0.00182801,0.001087652,0.0009409949,0.002028589],"category_scores_gemma":[0.021599,0.00059209,0.0008292083,0.001742042,0.001240002,0.001493086,0.001511706,0.001955643,0.001188604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001477591,"about_ca_system_score_gemma":0.002064211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003911984,"about_ca_topic_score_gemma":0.003809914,"domain_scores_codex":[0.9975322,0.0006012516,0.0001461964,0.0003545693,0.001157355,0.0002084052],"domain_scores_gemma":[0.9912487,0.006088093,0.000664808,0.0007293005,0.001133749,0.0001352111],"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":[0.0001693439,0.00005723515,0.001123269,0.0005197703,0.0001196221,0.0001124348,0.0001329547,0.7964965,0.009087902,0.09269091,0.0034998,0.09599023],"study_design_scores_gemma":[0.000006143103,0.00004980783,0.000345826,0.00007679799,0.00002125432,0.0001149258,0.00001734224,0.9752999,0.002962616,0.0175,0.003581642,0.00002373835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002193324,0.001325414,0.9942734,0.00009918732,0.00006022502,0.00001412707,0.00008046281,0.0002441171,0.001709775],"genre_scores_gemma":[0.5428715,0.006778807,0.4444715,0.0004698927,0.0006991629,0.0002848776,0.0007425599,0.000383725,0.003297987],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003911984,"threshold_uncertainty_score":0.01361644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02224639162321665,"score_gpt":0.3007901257696325,"score_spread":0.2785437341464159,"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."}}