{"id":"W4401634289","doi":"10.1109/tvt.2024.3445291","title":"Bayesian Cramer–Rao Bound, Extended and Unscented Kalman Filters Based Tracking Through Non-Ideal Transceivers in 5G and Beyond","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kalman filter; Ideal (ethics); Transceiver; Cramér–Rao bound; Bayesian probability; Tracking (education); Computer science; Filtering theory; Radar tracker; Control theory (sociology); Engineering; Electronic engineering; Telecommunications; Algorithm; Estimation theory; Artificial intelligence; Wireless; Radar","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001470973,0.000281356,0.0002920278,0.0007139226,0.0002566365,0.000245262,0.0002937266,0.0003605747,0.00001829109],"category_scores_gemma":[0.000003469543,0.0002904053,0.0001070394,0.001483053,0.0002980672,0.0004239595,0.00000255085,0.000760534,0.000008189214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009265205,"about_ca_system_score_gemma":0.00005580972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008916079,"about_ca_topic_score_gemma":0.0001687912,"domain_scores_codex":[0.9981804,0.00005848072,0.0003181984,0.0007817304,0.0002108924,0.0004502713],"domain_scores_gemma":[0.9993392,0.00008420814,0.00004130566,0.0003989355,0.0000409778,0.00009543817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008620305,0.0004269663,0.00004337046,0.0001484132,0.0002138326,0.001035501,0.0008867433,0.03101826,0.01077989,0.004052517,0.0001852696,0.9511231],"study_design_scores_gemma":[0.001495004,0.000445938,0.0002340762,0.0001973158,0.00005952976,0.0002667672,0.0003622749,0.9509163,0.03815096,0.003593942,0.003773109,0.0005047353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03216019,0.0005249092,0.9620519,0.003549704,0.0005749902,0.0002896278,0.00002697114,0.0006803522,0.0001413828],"genre_scores_gemma":[0.9923281,0.0002226801,0.007012204,0.0003020783,0.00001465331,0.00006000909,0.000003907619,0.00002544359,0.00003089163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9601679,"threshold_uncertainty_score":0.9999548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007371354892244242,"score_gpt":0.2325853286038249,"score_spread":0.2252139737115807,"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."}}