{"id":"W2132082977","doi":"10.1109/radar.2008.4720784","title":"Impact of measurement model mismatch on nonlinear Track-Before-Detect performance","year":2008,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Clutter; Computer science; Constant false alarm rate; Radar; Rayleigh distribution; Stationary target indication; Moving target indication; Sensitivity (control systems); Rayleigh scattering; Artificial intelligence; Radar tracker; Radar horizon; Algorithm; Radar imaging; Electronic engineering; Pulse-Doppler radar; Engineering; Telecommunications; Optics; Physics","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":[0.002566286,0.0006108985,0.0006716176,0.0002153519,0.0004622542,0.001081009,0.0004876572,0.001040926,0.001240217],"category_scores_gemma":[0.02169092,0.0003525502,0.0003061365,0.0003710549,0.0006240874,0.001716758,0.0008871027,0.0007601057,0.0005031473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000609298,"about_ca_system_score_gemma":0.001092823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002178253,"about_ca_topic_score_gemma":0.001808795,"domain_scores_codex":[0.9987426,0.0003105609,0.0001173238,0.0003160188,0.0003180542,0.0001955605],"domain_scores_gemma":[0.9934404,0.004577312,0.0005505282,0.0007429587,0.0005577962,0.0001309534],"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":[0.009131387,0.0004765272,0.04129479,0.0005324066,0.000307066,0.0008918961,0.0006812683,0.5952644,0.1604841,0.004947327,0.0006262269,0.1853625],"study_design_scores_gemma":[0.00009949164,0.001153333,0.01823074,0.00003303996,0.00008127848,0.0009301766,0.0001657006,0.8465856,0.1297904,0.002164845,0.0006679989,0.00009732372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8180398,0.0002601977,0.1790649,0.0002787311,0.00004945354,0.00007264113,0.0001157754,0.0006812621,0.001437289],"genre_scores_gemma":[0.9853817,0.00008333081,0.01393903,0.00005555864,0.000007032912,0.00002174185,0.000072028,0.00003179071,0.0004077838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002566286,"threshold_uncertainty_score":0.01357204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04638847759983412,"score_gpt":0.2586574277249303,"score_spread":0.2122689501250962,"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."}}