{"id":"W3082015495","doi":"10.1109/med48518.2020.9183366","title":"Distributed Time-varying Kalman Filter Design and Estimation over Wireless Sensor Networks Using OWA Sensor Fusion Technique","year":2020,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Kalman filter; Wireless sensor network; Computer science; Sensor fusion; Control theory (sociology); Gradient descent; Filter (signal processing); Fusion; Fuse (electrical); Algorithm; Artificial intelligence; Artificial neural network; Engineering; Computer vision","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":[0.001004513,0.0006846534,0.0007352091,0.0003901902,0.0004210813,0.0006353105,0.0008417412,0.0006905096,0.000539655],"category_scores_gemma":[0.002100948,0.0003464443,0.0006985655,0.0005048147,0.0005024018,0.001559751,0.0006915409,0.0009734584,0.0001784705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000591798,"about_ca_system_score_gemma":0.0008480065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004069028,"about_ca_topic_score_gemma":0.002712165,"domain_scores_codex":[0.9992687,0.0001766023,0.00005537342,0.0002313186,0.0002159522,0.00005195193],"domain_scores_gemma":[0.9994497,0.0002151209,0.00009012265,0.00005441349,0.0001734284,0.00001728498],"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":[0.00008824195,0.00003168773,0.000771457,0.0001123144,0.00006529266,0.00005948293,0.0001347232,0.8197678,0.01254388,0.01914909,0.0005361112,0.1467399],"study_design_scores_gemma":[0.000004657745,0.00002384148,0.0001004624,0.000003642855,0.000007986391,0.00001396103,0.000006162195,0.9955605,0.001519924,0.002227012,0.0005259193,0.000005947606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001383667,0.000052408,0.9983418,0.000017524,0.000008804442,0.000005167474,0.000003839438,0.00004700848,0.000139824],"genre_scores_gemma":[0.5927547,0.0007018378,0.4038128,0.00008303292,0.00008259176,0.000191977,0.0001187494,0.00004465164,0.002209639],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004069028,"threshold_uncertainty_score":0.008090675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02786805160746718,"score_gpt":0.2480823322390912,"score_spread":0.220214280631624,"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."}}