{"id":"W4404563518","doi":"10.1109/access.2024.3504338","title":"Strengthening Lattice Kalman Filters: Introducing Strong Tracking Lattice Filtering for Enhanced Robustness","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kalman filter; Robustness (evolution); Lattice (music); Control theory (sociology); Computer science; Lattice phase equaliser; Algorithm; Artificial intelligence; Physics; Acoustics; Adaptive filter; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002391773,0.0009467392,0.001017262,0.0007629981,0.0004749305,0.001464331,0.001158987,0.001118079,0.002036269],"category_scores_gemma":[0.00948488,0.0004206454,0.001048234,0.0008098886,0.001310928,0.002457881,0.001882904,0.002086995,0.0006885654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007770564,"about_ca_system_score_gemma":0.001594494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003555932,"about_ca_topic_score_gemma":0.002637165,"domain_scores_codex":[0.9984499,0.0004496936,0.0001189527,0.0002955982,0.0005540728,0.0001317988],"domain_scores_gemma":[0.9969485,0.001750386,0.0003631533,0.0003152517,0.0005312747,0.00009144778],"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":[0.0002097641,0.00006181154,0.001458952,0.0002780522,0.0001013834,0.0001455343,0.0002397163,0.6686254,0.02100235,0.124952,0.001568615,0.1813564],"study_design_scores_gemma":[0.00001441482,0.00006418725,0.0001091768,0.00001497064,0.000009668273,0.00003643733,0.00001186918,0.9851611,0.002882557,0.009880147,0.001790372,0.00002508123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001624573,0.00006960487,0.9976051,0.00004190693,0.00002849406,0.00000928287,0.00001281155,0.0001004729,0.0005076475],"genre_scores_gemma":[0.4787787,0.0007773722,0.5164525,0.0002499848,0.0002636321,0.0001800145,0.0001741103,0.0001575939,0.002966111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003555932,"threshold_uncertainty_score":0.01264906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04758195464275954,"score_gpt":0.3207339921799247,"score_spread":0.2731520375371652,"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."}}