{"id":"W4312990322","doi":"10.1109/tii.2022.3217533","title":"A Non-Line-of-Sight Mitigation Method for Indoor Ultra-Wideband Localization With Multiple Walls","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Non-line-of-sight propagation; Ranging; Ultra-wideband; Computer science; Line-of-sight; Radio propagation; Multilateration; Acoustics; Electronic engineering; Algorithm; Engineering; Wireless; Telecommunications; Physics; Aerospace engineering","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":[0.000241064,0.0006170683,0.0004392739,0.0004613338,0.0003571092,0.0003984225,0.0008100006,0.0005443998,0.001293489],"category_scores_gemma":[0.0005343821,0.0002701995,0.0006702486,0.0004006777,0.0003216947,0.000719082,0.000670393,0.0005016931,0.0005321164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002741236,"about_ca_system_score_gemma":0.0005664493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008891874,"about_ca_topic_score_gemma":0.001091521,"domain_scores_codex":[0.9997806,0.00003921848,0.00001212052,0.00004551202,0.0001061022,0.00001650986],"domain_scores_gemma":[0.9998189,0.00004450856,0.00003957617,0.00003214661,0.00005628729,0.000008698707],"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":[0.0001132433,0.0001253375,0.001444096,0.0003161502,0.00009980294,0.0004865954,0.0002890035,0.2955287,0.2176956,0.02631472,0.001886965,0.4556997],"study_design_scores_gemma":[0.00001717375,0.000134061,0.0005822442,0.00003011674,0.00004585367,0.0004831973,0.00005745927,0.9299802,0.05978638,0.002249574,0.006581393,0.0000524401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004987395,0.0000881531,0.9939889,0.00002215354,0.00002217218,0.00001079239,0.000004595223,0.0002058302,0.0006699868],"genre_scores_gemma":[0.2693245,0.0004468506,0.7240783,0.00007770106,0.00003436494,0.0001017607,0.00006886086,0.0001038004,0.005763794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001293489,"threshold_uncertainty_score":0.004327118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02238428836103049,"score_gpt":0.2421726660054115,"score_spread":0.219788377644381,"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."}}