{"id":"W4395069587","doi":"10.1109/access.2024.3393127","title":"A UHF Passive RFID Tag Position Estimation Approach Exploiting Mobile Robots: Phase-Only 3D Multilateration Particle Filters With No Unwrapping","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Scuola Superiore Sant'Anna; Università di Pisa; Canadian Institute for Advanced Research","keywords":"Multilateration; Ultra high frequency; Particle filter; Computer science; Position (finance); Robot; Phase (matter); Real-time computing; Acoustics; Telecommunications; Artificial intelligence; Kalman filter; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007542101,0.0001918894,0.0001499766,0.0001494302,0.0001345693,0.0005198208,0.0001655492,0.0001041041,0.00002709769],"category_scores_gemma":[0.00001788398,0.0001718569,0.00003728231,0.0004723033,0.00003990303,0.001494092,0.00002168632,0.0001633868,0.00004221271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001252267,"about_ca_system_score_gemma":0.00002286165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001644582,"about_ca_topic_score_gemma":0.000004243914,"domain_scores_codex":[0.9990036,0.00001966831,0.0002644125,0.0002677752,0.0001808084,0.0002637667],"domain_scores_gemma":[0.9996101,0.00004167908,0.00004239611,0.0001811862,0.00008497709,0.00003970119],"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":[0.00002006922,0.00004059345,0.0001265776,0.0002899881,0.00005392944,0.00001738878,0.0008587425,0.9325679,0.01991137,0.0001939854,0.0003765429,0.04554293],"study_design_scores_gemma":[0.000386231,0.00006753478,0.00003685794,0.0001714416,0.00002920143,0.00001813565,0.0002297577,0.8414255,0.1572759,0.0000507804,0.000101172,0.0002075264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3547122,0.00009409441,0.6427251,0.00002286885,0.0002885665,0.000327785,0.00001170141,0.00141447,0.0004032591],"genre_scores_gemma":[0.9927869,0.00002472075,0.006518461,0.0000420861,0.0001132555,0.0003173873,0.0001176938,0.00004975104,0.00002967974],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6380748,"threshold_uncertainty_score":0.7008119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01509193493078745,"score_gpt":0.2736793911985187,"score_spread":0.2585874562677312,"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."}}