{"id":"W2020499382","doi":"10.1109/icassp.2010.5495778","title":"Cram&amp;#x00E9;r-Rao lower bound for time reversal range estimators in N-multipath scattering environments","year":2010,"lang":"en","type":"article","venue":"","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Cramér–Rao bound; Estimator; Multipath propagation; Upper and lower bounds; Radar; Range (aeronautics); Algorithm; Estimation theory; Mathematics; Computer science; Electronic engineering; Statistics; Telecommunications; Engineering; Mathematical analysis","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000272005,0.0002525701,0.0002891565,0.0001949931,0.00007079955,0.00009611155,0.0002165448,0.0001113994,0.0005577253],"category_scores_gemma":[0.00003897022,0.0002602221,0.0001350641,0.0001289784,0.0000613504,0.0001619414,0.00004574664,0.0002736441,0.0005832968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007473328,"about_ca_system_score_gemma":0.000007493471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009988196,"about_ca_topic_score_gemma":0.0001078174,"domain_scores_codex":[0.998764,0.00001253838,0.0003091799,0.0003190855,0.0001373515,0.0004578311],"domain_scores_gemma":[0.9993467,0.00006490476,0.00002991931,0.0004341011,0.000008106924,0.000116233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001729302,0.00007799815,0.009360527,0.0000948837,0.00009873553,0.00001022891,0.0004043319,0.01216074,0.9663438,0.000006006493,0.007330506,0.004094901],"study_design_scores_gemma":[0.003527239,0.00006085033,0.0162962,0.0001956037,0.000210343,0.00004844992,0.00006978004,0.7692307,0.03855252,0.0001951477,0.1693248,0.002288348],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9730136,0.00003575463,0.02495947,0.0001551321,0.0004441241,0.000184165,0.00001811134,0.0002359617,0.0009536842],"genre_scores_gemma":[0.9726648,0.000008139595,0.02335731,0.0001060677,0.0001059979,0.00004461603,0.00002958564,0.00007934745,0.003604132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9277913,"threshold_uncertainty_score":0.999985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007134511264989107,"score_gpt":0.2163463665329256,"score_spread":0.2092118552679365,"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."}}