{"id":"W4416010421","doi":"10.1109/tsp.2025.3630236","title":"A Novel Robust Kalman Filter Based on Normal-Bernoulli Distribution for Non-Stationary Heavy-Tailed Measurement Noise","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Kalman filter; Bernoulli distribution; Noise (video); Control theory (sociology); Gaussian noise; Filter (signal processing); Gaussian; Noise measurement; Bernoulli's principle; Invariant extended Kalman filter","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001236946,0.0007764208,0.0006035951,0.0005391095,0.002669141,0.0009421251,0.0009229596,0.0004200106,0.0001476004],"category_scores_gemma":[0.000027463,0.000810667,0.0004679135,0.001614447,0.0002022716,0.00104421,0.00001007269,0.0009996652,0.00004322272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007322927,"about_ca_system_score_gemma":0.001207998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003453985,"about_ca_topic_score_gemma":0.00003116566,"domain_scores_codex":[0.9945388,0.0001643384,0.001224677,0.001592152,0.001443119,0.001036881],"domain_scores_gemma":[0.9965657,0.0005644863,0.0004329747,0.0008386516,0.001288647,0.0003095433],"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":[0.001398012,0.001967394,0.000009699871,0.0005231011,0.00007894477,0.000006897354,0.0002228947,0.7685881,0.002718064,0.00006882582,0.002391122,0.222027],"study_design_scores_gemma":[0.003359447,0.000640775,0.0001617117,0.003116435,0.0002322935,0.000008072574,0.00006910909,0.9629704,0.02592699,0.0001022926,0.002649695,0.0007627178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002861505,0.0001650056,0.9922137,0.002164407,0.002194685,0.00131568,0.000988027,0.0002658016,0.0004065213],"genre_scores_gemma":[0.9645563,0.00002249101,0.03242357,0.001658527,0.0002459817,0.0003866057,0.0001818353,0.00005951375,0.000465182],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9642701,"threshold_uncertainty_score":0.9994344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03769494711055527,"score_gpt":0.2612547067176327,"score_spread":0.2235597596070774,"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."}}