{"id":"W2884043659","doi":"10.1109/tcyb.2018.2850368","title":"Robust Consensus Nonlinear Information Filter for Distributed Sensor Networks With Measurement Outliers","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Outlier; Filter (signal processing); Computer science; Nonlinear system; Consensus; Gaussian; Estimator; Divergence (linguistics); Convergence (economics); Nonlinear filter; Information filtering system; Algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Machine learning; Statistics; Filter design; Multi-agent system","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":[0.00170973,0.0007025067,0.00126643,0.0007375561,0.0006389235,0.000855429,0.00139459,0.001297993,0.001016018],"category_scores_gemma":[0.005633766,0.0003255314,0.0007486459,0.001028793,0.0009032001,0.001666102,0.001095924,0.001227953,0.00030622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001431307,"about_ca_system_score_gemma":0.001633387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005731806,"about_ca_topic_score_gemma":0.002974864,"domain_scores_codex":[0.9986182,0.0002681846,0.0000730783,0.0003731695,0.0005644466,0.0001029557],"domain_scores_gemma":[0.9986038,0.0006438189,0.0002272962,0.0001035099,0.0003904698,0.00003115412],"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":[0.0001198773,0.0000313557,0.0004988167,0.0001278419,0.00005620515,0.0001073225,0.0001502896,0.8545257,0.00469731,0.03996167,0.0013325,0.09839113],"study_design_scores_gemma":[0.000008340661,0.00002219165,0.00007211357,0.000003615309,0.000005722347,0.00001569675,0.000006902831,0.993579,0.0006857851,0.005051498,0.000541461,0.000007618189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002651989,0.00009744256,0.9966151,0.00006012544,0.00001675065,0.0000122149,0.00001267019,0.0001118752,0.0004217724],"genre_scores_gemma":[0.7507346,0.0008244761,0.2418109,0.0001891189,0.0001373177,0.0003630981,0.0002462916,0.00008708041,0.005607226],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005731806,"threshold_uncertainty_score":0.01139694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03414656309257745,"score_gpt":0.2254839410680587,"score_spread":0.1913373779754813,"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."}}