{"id":"W2943747788","doi":"10.1049/iet-spr.2018.5400","title":"Non‐linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation","year":2019,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Agencia Nacional de Promoción Científica y Tecnológica; Universidad Nacional de Cuyo; Universidad Nacional de La Plata; Consejo Nacional de Investigaciones Científicas y Técnicas","keywords":"Kalman filter; Autoregressive model; Heteroscedasticity; Invariant extended Kalman filter; Extended Kalman filter; Autoregressive conditional heteroskedasticity; Clutter; Mathematics; Computer science; Algorithm; Statistics; Econometrics; Radar","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":[0.0002018275,0.0002411899,0.0002957632,0.0001103879,0.0002844516,0.0005049803,0.0005434939,0.0001261806,0.00005975197],"category_scores_gemma":[0.00002724521,0.0002169921,0.0001082157,0.0001567717,0.00006627895,0.001212555,0.0001033407,0.0002200097,0.00008169291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004092041,"about_ca_system_score_gemma":0.00007291753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004549405,"about_ca_topic_score_gemma":6.675372e-7,"domain_scores_codex":[0.998131,0.00004405002,0.0004265983,0.000590956,0.0003903758,0.0004169851],"domain_scores_gemma":[0.9988128,0.0002870533,0.0002957865,0.0003164216,0.0001742502,0.0001136943],"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":[0.0001962055,0.0003097473,0.004502429,0.0005251347,0.00008818555,0.00001917628,0.001760719,0.8554947,0.02038394,0.001998958,0.02777036,0.08695044],"study_design_scores_gemma":[0.0007810908,0.0001690664,0.0008742193,0.0001718728,0.00001871605,0.00001259825,0.00001458582,0.9907811,0.003740408,0.002252159,0.0008815833,0.0003025984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06940697,0.00006440908,0.9291779,0.00025938,0.0004631258,0.00035056,0.00003707361,0.0001748667,0.00006567731],"genre_scores_gemma":[0.777338,4.062678e-7,0.2212969,0.0006974797,0.0002007559,0.00004088587,0.0002605141,0.00002033688,0.0001447732],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.707931,"threshold_uncertainty_score":0.8848681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02513792227808843,"score_gpt":0.2920695900591675,"score_spread":0.2669316677810791,"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."}}