{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001363591,0.0009331155,0.001701834,0.000745696,0.000487905,0.001189202,0.001135942,0.001378389,0.001729796],"category_scores_gemma":[0.002623198,0.0008029125,0.0008622179,0.0009087758,0.0006079519,0.00165229,0.001438853,0.0009829177,0.000687803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004884453,"about_ca_system_score_gemma":0.0009427417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003844512,"about_ca_topic_score_gemma":0.003660144,"domain_scores_codex":[0.9993279,0.0001902622,0.0000407372,0.0001521536,0.0002320074,0.00005688407],"domain_scores_gemma":[0.9989893,0.000413908,0.00009779288,0.0001332998,0.0003316684,0.00003402567],"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":[0.0004544135,0.0001105,0.0007524614,0.0002236857,0.0002495172,0.0001143097,0.0001267193,0.599638,0.02601657,0.04214068,0.002709376,0.3274638],"study_design_scores_gemma":[0.000008644774,0.00003731771,0.00009797687,0.000004093867,0.00001527625,0.00002057172,0.000003545191,0.9944978,0.00172018,0.002679172,0.0009040879,0.00001136209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002298207,0.0001378279,0.9969262,0.00004279171,0.00002910901,0.000007870162,0.000009872618,0.0001074328,0.0004406556],"genre_scores_gemma":[0.3838682,0.0005628553,0.6065543,0.0001728253,0.000177186,0.0001742109,0.0002276323,0.0001962613,0.008066553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003844512,"threshold_uncertainty_score":0.007644236,"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."}}