{"id":"W2799362944","doi":"10.1109/dinwc.2018.8356991","title":"Effect of UWB channel time delay parameters on TDOA localization","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Multilateration; Non-line-of-sight propagation; Computer science; Channel (broadcasting); Time of arrival; FDOA; Delay spread; Real-time computing; Line-of-sight; Sight; Line (geometry); Electronic engineering; Telecommunications; Acoustics; Wireless; Engineering; Mathematics; Multipath propagation; Physics","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":[0.0007195398,0.0006986544,0.0003672237,0.0006598007,0.000338021,0.0006623234,0.0003746767,0.0005352607,0.0007567285],"category_scores_gemma":[0.006842281,0.0002721476,0.0002789747,0.0006791108,0.0005475184,0.001237156,0.000424044,0.0005614145,0.0002116402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005905207,"about_ca_system_score_gemma":0.0004960579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001744969,"about_ca_topic_score_gemma":0.001385601,"domain_scores_codex":[0.9990596,0.0002441135,0.00004485908,0.0001838919,0.0003294503,0.0001380114],"domain_scores_gemma":[0.9942117,0.004326717,0.0004407491,0.0003856972,0.0005704873,0.00006462391],"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":[0.001178039,0.00007967727,0.01408227,0.0007089702,0.0001726604,0.001330367,0.0007907452,0.6477112,0.2023082,0.006649544,0.0005800383,0.1244083],"study_design_scores_gemma":[0.0000711523,0.0005857653,0.01139914,0.0001276522,0.0003807324,0.002704456,0.0007575744,0.6308731,0.3430261,0.003994823,0.00588564,0.0001937631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3587636,0.001812375,0.6331798,0.0001959546,0.0001930804,0.00003484799,0.0001720245,0.0009884271,0.004659826],"genre_scores_gemma":[0.9810154,0.0005441009,0.01775452,0.00003293274,0.00001574952,0.00001868309,0.00006218383,0.0001112397,0.0004450574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001744969,"threshold_uncertainty_score":0.004284501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004843699972772286,"score_gpt":0.2100232251080886,"score_spread":0.2051795251353163,"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."}}