{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001101976,0.00009696918,0.00009001463,0.00006406061,0.00005252373,0.000008635786,0.0002475235,0.00008196212,0.00002264488],"category_scores_gemma":[0.0002368415,0.00008841612,0.00001601454,0.0001482306,0.00002291697,0.0000634281,0.000064121,0.00006126384,0.00002156942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003019763,"about_ca_system_score_gemma":0.00001008821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002789682,"about_ca_topic_score_gemma":0.00007518447,"domain_scores_codex":[0.9994038,0.000007346534,0.0001368083,0.0001404644,0.0001345574,0.0001770672],"domain_scores_gemma":[0.9994366,0.00001462749,0.00001553116,0.00039097,0.0001141525,0.00002818111],"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":[0.0004076642,0.0004002463,0.004894242,0.0009892803,0.0007515358,0.00006581474,0.01088862,0.07373768,0.1270285,0.006055713,0.1379814,0.6367993],"study_design_scores_gemma":[0.0007697558,0.00005136356,0.0007379223,0.00002552713,0.00002679141,0.000002928569,0.000581792,0.7202461,0.2147645,0.000177421,0.06232518,0.000290631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5851822,0.0006900104,0.3935928,0.000127683,0.003718194,0.001642535,0.001026435,0.005825362,0.008194729],"genre_scores_gemma":[0.9928362,0.00003242828,0.006854487,0.00001863759,0.00007537775,0.00001204231,0.00009881614,0.00002827233,0.00004377122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6465085,"threshold_uncertainty_score":0.3605505,"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."}}