{"id":"W2165996196","doi":"10.1117/12.478508","title":"&lt;title&gt;Comparison of EKF, pseudomeasurement, and particle filters for a bearing-only target tracking problem&lt;/title&gt;","year":2002,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Extended Kalman filter; Particle filter; Linearization; Tracking (education); Control theory (sociology); Computer science; Invariant extended Kalman filter; Estimator; Nonlinear system; Kalman filter; Filter (signal processing); Monte Carlo method; Radar tracker; Nonlinear filter; Bearing (navigation); Filter design; Mathematics; Computer vision; Artificial intelligence; Radar; Physics; Telecommunications","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":[0.001753942,0.0004329242,0.0007514335,0.0003984325,0.0002170774,0.0007153313,0.0004734299,0.0009848225,0.0009920261],"category_scores_gemma":[0.005672339,0.00015481,0.0003670646,0.0004170666,0.0002865529,0.001180034,0.0002666319,0.0004540267,0.0003656724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004032356,"about_ca_system_score_gemma":0.0006563172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005280795,"about_ca_topic_score_gemma":0.003425837,"domain_scores_codex":[0.9995013,0.0001842734,0.00003672382,0.00007387873,0.000167467,0.00003624804],"domain_scores_gemma":[0.9974551,0.001864822,0.00008857361,0.0001525623,0.0004088469,0.00003019402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008030009,0.0001558514,0.003339401,0.0003084988,0.0001516428,0.00008706643,0.00008792371,0.4408315,0.009471581,0.009778976,0.001902051,0.5330825],"study_design_scores_gemma":[0.00003572813,0.00009499583,0.001090277,0.00001459642,0.00001992893,0.00004419883,0.0000121674,0.99161,0.00497341,0.001076979,0.001011214,0.00001663737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0402358,0.001494584,0.9556448,0.0002448726,0.0001044723,0.00003933164,0.00005055999,0.0005357024,0.001649752],"genre_scores_gemma":[0.3848858,0.001691702,0.6103628,0.0001366357,0.00008002342,0.0001105198,0.0002957702,0.0001305162,0.002306323],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005280795,"threshold_uncertainty_score":0.01050013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0249738202700299,"score_gpt":0.2402039904374721,"score_spread":0.2152301701674423,"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."}}