{"id":"W2763651857","doi":"10.1109/ccta.2017.8062647","title":"Norm- and linear-inequality-constrained state estimation: An LMI approach","year":2017,"lang":"en","type":"article","venue":"2017 IEEE Conference on Control Technology and Applications (CCTA)","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Linear matrix inequality; Norm (philosophy); Control theory (sociology); Convex optimization; Mathematics; Observer (physics); Regular polygon; Filter (signal processing); State (computer science); Mathematical optimization; Inequality; Computer science; Algorithm; Control (management); 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":[0.001438506,0.001699387,0.001418417,0.0007935022,0.0005989987,0.00129187,0.001325501,0.001114133,0.004954768],"category_scores_gemma":[0.003045276,0.0007080983,0.0009349319,0.0007294067,0.001016443,0.001560085,0.0009190973,0.0026385,0.001817585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001022576,"about_ca_system_score_gemma":0.001440554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003909078,"about_ca_topic_score_gemma":0.00358942,"domain_scores_codex":[0.999046,0.0002859582,0.00007609217,0.0001899917,0.0003464815,0.00005543864],"domain_scores_gemma":[0.998603,0.0008309177,0.0001527618,0.00009262239,0.0003021729,0.00001853229],"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":[0.00008656631,0.0001101377,0.0003161349,0.0006689801,0.0001367621,0.0002207157,0.0002464935,0.687408,0.0122229,0.07180265,0.005952277,0.2208284],"study_design_scores_gemma":[0.00001063395,0.0000444229,0.00008740012,0.00002891657,0.00001411278,0.00003558553,0.00001274918,0.9865781,0.002993226,0.006514331,0.003662187,0.00001840931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002033788,0.00005054389,0.9982576,0.00004353987,0.00001780862,0.00001614466,0.00001463007,0.0001469147,0.001249451],"genre_scores_gemma":[0.217462,0.001057467,0.7700502,0.0003248843,0.0003445284,0.0009363717,0.0003932219,0.0003785031,0.009052819],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004954768,"threshold_uncertainty_score":0.01657534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03732196391309883,"score_gpt":0.3017448969828374,"score_spread":0.2644229330697385,"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."}}