{"id":"W4319160361","doi":"10.1016/j.inffus.2023.01.025","title":"Multi-sensor integrated navigation/positioning systems using data fusion: From analytics-based to learning-based approaches","year":2023,"lang":"en","type":"article","venue":"Information Fusion","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":233,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Basic and Applied Basic Research Foundation of Guangdong Province","keywords":"Computer science; Sensor fusion; GNSS applications; Kalman filter; Artificial intelligence; Real-time computing; Global Positioning System; Analytics; Simultaneous localization and mapping; Navigation system; Data mining; Robot; Mobile robot; Telecommunications","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":[0.001461093,0.001117886,0.001015994,0.001844584,0.0003832222,0.002132877,0.001276782,0.001169685,0.000663958],"category_scores_gemma":[0.001820506,0.0004247133,0.0007622176,0.002686664,0.001153191,0.003858646,0.002382161,0.001467941,0.0003432432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008559471,"about_ca_system_score_gemma":0.000866243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001607924,"about_ca_topic_score_gemma":0.001114587,"domain_scores_codex":[0.9988483,0.0002678022,0.00008725328,0.0002495048,0.0004746128,0.00007241005],"domain_scores_gemma":[0.9991602,0.0002884352,0.0001369462,0.0001467585,0.0002225278,0.00004515824],"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":[0.0001388848,0.0001407652,0.004118244,0.001018642,0.0003383781,0.0002598597,0.0005628524,0.1894853,0.01480228,0.1209374,0.003677657,0.6645198],"study_design_scores_gemma":[0.0000215552,0.0002531185,0.001757388,0.0003432878,0.0001415664,0.000273802,0.0004032795,0.8326539,0.01463137,0.1164997,0.03290785,0.0001130804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004632306,0.008093229,0.9830925,0.0007453975,0.0001383337,0.00004847488,0.00005112497,0.00034157,0.002857015],"genre_scores_gemma":[0.5110045,0.02410642,0.4600438,0.0006100587,0.0009277819,0.0001675102,0.0003732872,0.000112051,0.002654583],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002132877,"threshold_uncertainty_score":0.007727087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07829286667483623,"score_gpt":0.2641208792706943,"score_spread":0.185828012595858,"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."}}