{"id":"W2168974696","doi":"10.3390/s110706771","title":"Data Fusion Algorithms for Multiple Inertial Measurement Units","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":138,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial measurement unit; Filter (signal processing); Global Positioning System; Sensor fusion; Computer science; Frame (networking); Inertial navigation system; Context (archaeology); Computer vision; Kalman filter; Extended Kalman filter; Artificial intelligence; Units of measurement; Real-time computing; Inertial frame of reference; Telecommunications; Geography","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":[0.002350902,0.001019992,0.001176315,0.001582589,0.0007435286,0.001427416,0.001372032,0.00115193,0.001875444],"category_scores_gemma":[0.004953518,0.0005628858,0.001186334,0.002243343,0.0004791894,0.002622257,0.001480602,0.001246637,0.001080798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008927538,"about_ca_system_score_gemma":0.00100972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003513568,"about_ca_topic_score_gemma":0.002647434,"domain_scores_codex":[0.9985071,0.0002617632,0.0001552665,0.0003296646,0.0006631319,0.00008313989],"domain_scores_gemma":[0.9987099,0.0003652595,0.000160742,0.0001633655,0.0005766135,0.00002410065],"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":[0.0001482533,0.00004825948,0.001241995,0.0002802227,0.0002031205,0.0000866736,0.0002297282,0.1940986,0.009999226,0.03854849,0.003471694,0.7516437],"study_design_scores_gemma":[0.00002763554,0.0001091055,0.001042536,0.00007315991,0.00008656477,0.0001361712,0.00006134422,0.9439055,0.01220335,0.02350678,0.01879324,0.00005459957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001523503,0.0005855436,0.9969684,0.00007506853,0.0000673861,0.00002280968,0.00002415702,0.0002641216,0.0004689623],"genre_scores_gemma":[0.1338253,0.002063885,0.8596126,0.0001393209,0.0002027525,0.0002769523,0.0003193909,0.00009137917,0.003468399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003513568,"threshold_uncertainty_score":0.01243287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1938919816162655,"score_gpt":0.2551990833832756,"score_spread":0.0613071017670101,"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."}}