{"id":"W4393150239","doi":"10.3390/s24072087","title":"A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Observability; Control theory (sociology); Observer (physics); Kalman filter; Monte Carlo method; Extended Kalman filter; Iterated function; Alpha beta filter; Filter (signal processing); Computer science; Position (finance); Nonlinear system; Algorithm; Engineering; Mathematics; Artificial intelligence; Computer vision; Moving horizon estimation; Physics; Applied mathematics","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":[0.0008466024,0.0005657786,0.000926166,0.000503184,0.0004569638,0.0005763052,0.001170233,0.0008395232,0.001016501],"category_scores_gemma":[0.003113203,0.0004266611,0.0007501617,0.0004874845,0.0004922051,0.001020445,0.000601667,0.001142948,0.0003370185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006729089,"about_ca_system_score_gemma":0.001696682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01314437,"about_ca_topic_score_gemma":0.01255199,"domain_scores_codex":[0.9994953,0.00008479806,0.00003489184,0.0001209207,0.0002169177,0.0000471356],"domain_scores_gemma":[0.9990678,0.0004639364,0.0001164287,0.00007662857,0.0002457164,0.00002947909],"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":[0.0001022488,0.00004413136,0.001614976,0.00009270682,0.00007672542,0.00006909962,0.0000998987,0.843711,0.007493172,0.01012271,0.0007579055,0.1358154],"study_design_scores_gemma":[0.000003802262,0.0000177249,0.000165516,0.000004642325,0.000006491917,0.00001827951,0.00000241721,0.9976884,0.0008252559,0.0006850406,0.0005747226,0.000007618522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002632692,0.0001059544,0.996685,0.00002278285,0.00001909557,0.00001031249,0.00001026414,0.0001572286,0.0003566974],"genre_scores_gemma":[0.5055146,0.0006044697,0.4903027,0.0001198848,0.000115546,0.0001837348,0.0002568934,0.0001093259,0.002792904],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01314437,"threshold_uncertainty_score":0.02613574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02149536594404253,"score_gpt":0.270084338901958,"score_spread":0.2485889729579155,"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."}}