{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000372849,0.00009285291,0.0001344463,0.0001231249,0.00005922342,0.00008073729,0.000284095,0.00005116286,0.00001574691],"category_scores_gemma":[0.0001821529,0.0000826895,0.0000477187,0.0002162899,0.00002131512,0.001093382,0.00006124398,0.00003569995,0.00002745054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005206166,"about_ca_system_score_gemma":0.00003009982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007065197,"about_ca_topic_score_gemma":9.569786e-7,"domain_scores_codex":[0.9991695,0.00002454105,0.0002556241,0.0001476327,0.0002139292,0.0001888317],"domain_scores_gemma":[0.9991191,0.0002160275,0.0001382434,0.0003217361,0.0001339057,0.00007100437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000757067,0.0001511651,0.0002499909,0.0001734307,0.00002452237,1.074863e-7,0.0004916046,0.02510943,0.001385766,0.9291946,0.009698126,0.0335137],"study_design_scores_gemma":[0.0001293049,0.0000554813,0.0006499423,0.0000367285,0.00000743269,0.000004462105,0.000008014982,0.9856261,0.008308149,0.004327872,0.0007228182,0.0001237121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001435716,0.00004059313,0.9945854,0.0000694124,0.0007425694,0.0004466939,0.00000283161,0.0005291781,0.002147629],"genre_scores_gemma":[0.7309526,3.529652e-7,0.268575,0.00003550801,0.0000454015,0.0000898463,0.000002445297,0.000006634325,0.0002921866],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9605166,"threshold_uncertainty_score":0.337198,"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."}}