{"id":"W1994250836","doi":"10.1155/2014/451939","title":"An Adaptive Unscented Kalman Filtering Algorithm for MEMS/GPS Integrated Navigation Systems","year":2014,"lang":"en","type":"article","venue":"Journal of Applied Mathematics","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"National Natural Science Foundation of China","keywords":"Kalman filter; Computer science; Global Positioning System; Estimator; Navigation system; Noise (video); Statistic; Nonlinear system; Control theory (sociology); Algorithm; Artificial intelligence; Mathematics; Statistics; 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":[0.000490685,0.0005981802,0.0006307708,0.0003681129,0.0004426003,0.0004779985,0.0006817504,0.0005896768,0.001447977],"category_scores_gemma":[0.00115139,0.0002745693,0.0005193888,0.0006243986,0.0003157278,0.0007801731,0.0005584097,0.0008550835,0.0004556413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004759672,"about_ca_system_score_gemma":0.001060073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008359956,"about_ca_topic_score_gemma":0.005143074,"domain_scores_codex":[0.9995204,0.00009102697,0.00004059006,0.0001003836,0.0002191896,0.00002830114],"domain_scores_gemma":[0.9997396,0.00006583882,0.00003909178,0.00001651876,0.0001313378,0.000007563097],"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":[0.0001445242,0.00004316663,0.001024515,0.0002717423,0.00008522608,0.00008910687,0.0001477914,0.5465024,0.01906154,0.01899294,0.003706318,0.4099306],"study_design_scores_gemma":[0.0000111639,0.00004171819,0.0002363839,0.00001153496,0.00001170931,0.00002721942,0.000008137627,0.9924844,0.002173723,0.001881412,0.003102071,0.00001043986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002303694,0.0003542193,0.996381,0.00005155852,0.00005294828,0.00001596197,0.00001917076,0.0001927635,0.0006287485],"genre_scores_gemma":[0.397186,0.001956816,0.591914,0.000167078,0.0001969544,0.000359387,0.0003999277,0.00008640596,0.007733406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008359956,"threshold_uncertainty_score":0.0166226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01961046617133197,"score_gpt":0.2525096095322598,"score_spread":0.2328991433609278,"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."}}