{"id":"W2154394329","doi":"10.1109/tap.2007.901862","title":"Mobile Terminal Location for MIMO Communication Systems","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Base station; Multipath propagation; Computer science; Terminal (telecommunication); Position (finance); Context (archaeology); Cramér–Rao bound; Angle of arrival; Delay spread; MIMO; Mobile station; Mobile telephony; Direction of arrival; Square root; SIGNAL (programming language); Root mean square; Mean squared error; Set (abstract data type); Algorithm; Telecommunications; Estimation theory; Mobile radio; Mathematics; Statistics; Electrical engineering; Engineering; Geometry; Antenna (radio)","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":[0.000281313,0.0006774928,0.0004969417,0.0003349063,0.0003848631,0.0006456532,0.0004786167,0.0008440631,0.002562311],"category_scores_gemma":[0.001399483,0.0001882386,0.000293424,0.0005348374,0.0003117444,0.0006632996,0.0005215811,0.0007482533,0.001918599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003231243,"about_ca_system_score_gemma":0.0003531857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008169046,"about_ca_topic_score_gemma":0.001361941,"domain_scores_codex":[0.9996276,0.0001228086,0.00001358134,0.00007496851,0.0001295323,0.00003155219],"domain_scores_gemma":[0.9996285,0.000153638,0.00005236177,0.00006113118,0.00009197861,0.00001233171],"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":[0.0002473565,0.00005124503,0.001686731,0.0004801039,0.00007420625,0.0008395605,0.0002137361,0.4760138,0.05313297,0.1204269,0.008996787,0.3378367],"study_design_scores_gemma":[0.00003150573,0.0001825936,0.0006353068,0.00005614811,0.00003395188,0.0006609473,0.00005370397,0.9439573,0.009550529,0.02659513,0.01819554,0.0000473132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004513064,0.00116101,0.9903933,0.000172234,0.0001948929,0.00001884121,0.00005927481,0.0003415864,0.003145803],"genre_scores_gemma":[0.5385849,0.004058798,0.4435147,0.0003876653,0.0007748228,0.0001835753,0.0003768531,0.00007473068,0.01204395],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002562311,"threshold_uncertainty_score":0.008571804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01249238627671659,"score_gpt":0.2387459541325298,"score_spread":0.2262535678558132,"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."}}