{"id":"W4293095119","doi":"10.1109/vtc2022-spring54318.2022.9860999","title":"Positioning and Tracking Using Reconfigurable Intelligent Surfaces and Extended Kalman Filter","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Multilateration; FDOA; Computer science; Kalman filter; User equipment; Telecommunications link; Real-time computing; Non-line-of-sight propagation; Base station; Path loss; Tracking (education); Electronic engineering; Telecommunications; Wireless; Engineering; Artificial intelligence; Mathematics; Azimuth","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":[0.0004625802,0.0007018513,0.0007367163,0.0004737669,0.0003234658,0.00075707,0.0007793601,0.0007656282,0.0007606471],"category_scores_gemma":[0.001098466,0.0003444154,0.0007260325,0.0006676475,0.0005580476,0.001038704,0.0008442538,0.0006233039,0.0003781086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006589118,"about_ca_system_score_gemma":0.0007133812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01514494,"about_ca_topic_score_gemma":0.007488413,"domain_scores_codex":[0.9995634,0.00008976481,0.00002138496,0.0001301373,0.0001464069,0.00004891089],"domain_scores_gemma":[0.9996308,0.0001397417,0.00007037534,0.00006066768,0.00008435494,0.00001396929],"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":[0.00008766747,0.00001886826,0.0009694807,0.00003899298,0.00004859841,0.00007149413,0.00006237523,0.91392,0.005227583,0.003760777,0.0003694397,0.07542478],"study_design_scores_gemma":[0.000006127383,0.00002143265,0.0001730805,0.000002477957,0.00000625236,0.00001145712,0.000004847584,0.9981859,0.000651871,0.0006257195,0.0003048163,0.000005925636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01283187,0.0001271643,0.9853816,0.00004999712,0.00002426485,0.00001035277,0.0000174037,0.0005166553,0.001040601],"genre_scores_gemma":[0.7780487,0.0003146234,0.2183772,0.00006511089,0.00003654044,0.00008168339,0.0001423849,0.00004644859,0.002887212],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01514494,"threshold_uncertainty_score":0.03011358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02531194577101224,"score_gpt":0.2497702327741162,"score_spread":0.2244582870031039,"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."}}