{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002991382,0.0002486675,0.0001989351,0.0001137586,0.0004001469,0.0002375425,0.0003931781,0.0001544036,0.00002643108],"category_scores_gemma":[0.00001577132,0.0002128816,0.00008941555,0.0003777007,0.0001651417,0.0002320528,0.000004073423,0.000269846,0.00007494666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009439827,"about_ca_system_score_gemma":0.0000737529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002119283,"about_ca_topic_score_gemma":0.00007100317,"domain_scores_codex":[0.9982713,0.00005556241,0.0003934613,0.0003398512,0.0004995347,0.0004402893],"domain_scores_gemma":[0.9981397,0.0001677421,0.0001439868,0.0006606009,0.0007213865,0.0001665924],"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":[0.0002587207,0.0001762689,0.00001464366,0.00001747992,0.0000972879,0.000003937771,0.0003867091,0.9454039,0.00003882501,0.0001874853,0.01324487,0.04016983],"study_design_scores_gemma":[0.001343912,0.0006678775,0.00006975378,0.00006591684,0.0000585505,0.00003153351,0.00007000236,0.9523543,0.002950332,0.00002348171,0.04199125,0.000373084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001188634,0.00001055469,0.9955142,0.0005443385,0.00143387,0.0004586322,0.0003626682,0.0003020517,0.0001850406],"genre_scores_gemma":[0.6774311,0.00002442815,0.3212245,0.0007608752,0.0002639108,0.00006399364,0.00009509342,0.00002552359,0.0001105579],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6762425,"threshold_uncertainty_score":0.8681061,"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."}}