{"id":"W2002910452","doi":"10.1016/j.sigpro.2013.08.015","title":"Combined cubature Kalman and smooth variable structure filtering: A robust nonlinear estimation strategy","year":2013,"lang":"en","type":"article","venue":"Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ford Motor Company (Canada); McMaster University","funders":"","keywords":"Kalman filter; Robustness (evolution); Control theory (sociology); Nonlinear system; Mathematics; Gaussian; State variable; Algorithm; Computer science; Statistics; Artificial intelligence","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":[0.0001120478,0.0001928958,0.000174016,0.00007279596,0.0003344603,0.001020469,0.0004069226,0.0001403446,0.000139047],"category_scores_gemma":[0.00001687046,0.0001641239,0.00001916888,0.0003481693,0.00005239077,0.001290562,0.000163377,0.0003158451,0.00001052423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001571323,"about_ca_system_score_gemma":0.00007232958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004052657,"about_ca_topic_score_gemma":0.000001464699,"domain_scores_codex":[0.9987449,0.00003884102,0.0002335139,0.0004291825,0.0002375896,0.0003159912],"domain_scores_gemma":[0.9993041,0.00005228426,0.0001273189,0.0002569447,0.0001348716,0.0001245448],"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":[0.00002515387,0.000103529,0.0004448974,0.0003799197,0.00002347301,0.00002175929,0.0008951043,0.2818468,0.02128066,0.005629314,0.00500549,0.6843439],"study_design_scores_gemma":[0.0002758797,0.0000705429,0.000746725,0.0001234579,0.000006733118,0.0000227568,0.00003701635,0.9897761,0.00047831,0.007787885,0.0004539806,0.0002206022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04503848,0.0003174903,0.952987,0.0002709585,0.0001452988,0.0001957014,0.0000109086,0.000305126,0.0007291068],"genre_scores_gemma":[0.6728747,0.000003345676,0.3267186,0.0001654233,0.0001028687,0.000005093195,0.00003706778,0.00001277244,0.00008018103],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7079293,"threshold_uncertainty_score":0.9840405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0132410885447598,"score_gpt":0.2223789842309562,"score_spread":0.2091378956861964,"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."}}