{"id":"W2787106128","doi":"10.1109/taes.2017.2760778","title":"Multipath Maximum Likelihood Probabilistic Multihypothesis Tracker for Low Observable Targets","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hamilton Health Sciences","funders":"China Postdoctoral Science Foundation","keywords":"Multipath propagation; Probabilistic logic; Computer science; Radar tracker; Multipath mitigation; Algorithm; Observable; Radar; Artificial intelligence; Telecommunications; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005128005,0.0003359544,0.0004122461,0.00008192052,0.001495921,0.000831134,0.000865514,0.000211001,0.000007224593],"category_scores_gemma":[0.00003224366,0.0003001333,0.0001488922,0.0001070693,0.00009183,0.0005114079,0.000007175739,0.0003601168,0.00003719507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001253501,"about_ca_system_score_gemma":0.0001201789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003296407,"about_ca_topic_score_gemma":0.0003187183,"domain_scores_codex":[0.997511,0.00007939302,0.0003515842,0.0008147722,0.0002797158,0.0009635261],"domain_scores_gemma":[0.9977491,0.0002872536,0.0002331792,0.001397044,0.0001285795,0.0002048254],"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":[0.001520701,0.005249318,0.001014962,0.002739565,0.001566856,0.00009315548,0.006370747,0.1567369,0.04193662,0.02112201,0.02126687,0.7403823],"study_design_scores_gemma":[0.004783958,0.001260658,0.0006551741,0.0005647448,0.0001534825,0.0001195826,0.0002182849,0.952709,0.01327177,0.002582534,0.02217096,0.0015098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02877179,0.0007760801,0.9663149,0.0006452131,0.002011055,0.001063222,0.00006621968,0.000256854,0.0000946725],"genre_scores_gemma":[0.9923337,0.000391817,0.005614993,0.00008707942,0.0001523132,0.0003819038,0.000003857805,0.00004264399,0.0009917432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9635618,"threshold_uncertainty_score":0.9999451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01918310928162465,"score_gpt":0.2420409515528386,"score_spread":0.222857842271214,"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."}}