{"id":"W3010992090","doi":"10.1109/camsap45676.2019.9022674","title":"Blind Maximum Likelihood Jade in Multipath Environement Using Importance Sampling","year":2019,"lang":"en","type":"article","venue":"","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Cramér–Rao bound; Computer science; Maximum likelihood; Multipath propagation; Algorithm; Maximization; Sampling (signal processing); Upper and lower bounds; Joint (building); Estimation theory; Expectation–maximization algorithm; Importance sampling; Signal-to-noise ratio (imaging); SIGNAL (programming language); Mathematical optimization; Iterative method; Statistics; Mathematics; Telecommunications; Monte Carlo method; Detector; Channel (broadcasting); Engineering","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":[0.001617679,0.0006149579,0.00103018,0.0004881695,0.0003004705,0.0008811883,0.0006536813,0.000773085,0.0006771891],"category_scores_gemma":[0.00664554,0.0005106421,0.0004532633,0.0006557241,0.000780835,0.001290194,0.001375669,0.0008750791,0.0002630396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002873238,"about_ca_system_score_gemma":0.0007795477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008399856,"about_ca_topic_score_gemma":0.001033457,"domain_scores_codex":[0.9991614,0.0004581751,0.00003577675,0.00008950796,0.0001924946,0.00006280602],"domain_scores_gemma":[0.9969243,0.002465909,0.0001675268,0.0001473521,0.000234625,0.00006019299],"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":[0.0004956858,0.00007501948,0.001265415,0.0002968972,0.0001243651,0.0001775871,0.0001792939,0.8017207,0.01558607,0.03203578,0.001048129,0.146995],"study_design_scores_gemma":[0.00002238144,0.00004494693,0.000192855,0.000008868716,0.000009078563,0.0000605093,0.00001269343,0.9908997,0.003288585,0.004950769,0.0004981929,0.00001144211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006692117,0.000174762,0.9927462,0.00003649685,0.00001311304,0.000007901628,0.000006822655,0.00006876459,0.0002538152],"genre_scores_gemma":[0.3446702,0.0004753968,0.6526788,0.00007236257,0.00007791601,0.00008974701,0.0001086789,0.00007068684,0.001756171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001617679,"threshold_uncertainty_score":0.008555233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03499129999447652,"score_gpt":0.2973486051363584,"score_spread":0.2623573051418818,"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."}}