{"id":"W1966184693","doi":"10.1109/tsp.2007.914964","title":"Square-Root Quadrature Kalman Filtering","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":155,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"U.S. Department of Homeland Security","keywords":"Extended Kalman filter; Mathematics; Kalman filter; Clenshaw–Curtis quadrature; Gauss–Hermite quadrature; Fast Kalman filter; Invariant extended Kalman filter; Adaptive quadrature; Gauss–Kronrod quadrature formula; Ensemble Kalman filter; Square root; Quadrature (astronomy); Control theory (sociology); Covariance intersection; Alpha beta filter; Tanh-sinh quadrature; Applied mathematics; Covariance matrix; Algorithm; Gaussian quadrature; Computer science; Mathematical analysis; Statistics; Artificial intelligence; Engineering; Integral equation; Electronic engineering; Nyström method; Geometry; Moving horizon estimation","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":[0.001292046,0.0006060583,0.00125682,0.0004427432,0.0004379579,0.001306478,0.001192521,0.0009702736,0.003686765],"category_scores_gemma":[0.005906325,0.0003350514,0.0006215223,0.0009015245,0.0007213452,0.001616473,0.001100912,0.001059898,0.00189331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006047364,"about_ca_system_score_gemma":0.001199047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003404154,"about_ca_topic_score_gemma":0.002838412,"domain_scores_codex":[0.9988187,0.0002490113,0.00005496397,0.0002872448,0.0004884792,0.0001014242],"domain_scores_gemma":[0.9984749,0.0004917072,0.0001181538,0.0002832814,0.0005950792,0.00003672237],"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":[0.0001836722,0.00006944974,0.001689721,0.0003190559,0.0001415928,0.0001578988,0.0001834036,0.3887161,0.01406758,0.2117818,0.01243666,0.370253],"study_design_scores_gemma":[0.00002077744,0.00004076893,0.0003264131,0.00002516455,0.00002485556,0.00007324864,0.0000184686,0.9424977,0.003049626,0.04110988,0.01279142,0.00002163261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001262719,0.0002194682,0.9958488,0.00007072352,0.00006250294,0.00001572017,0.00004756946,0.0003354887,0.00213694],"genre_scores_gemma":[0.3624599,0.001950984,0.6200131,0.0004069428,0.0003290893,0.0001842073,0.0007917146,0.0003204453,0.01354364],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003686765,"threshold_uncertainty_score":0.01233345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02674836540943531,"score_gpt":0.2465436623518149,"score_spread":0.2197952969423796,"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."}}