{"id":"W2556886647","doi":"10.1109/taes.2016.140820","title":"Passive tracking in heavy clutter with sensor location uncertainty","year":2016,"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":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; McMaster University","funders":"","keywords":"Clutter; Initialization; Computer science; Iterative method; Algorithm; Radar tracker; Sonar; Likelihood-ratio test; Covariance; Real-time computing; Artificial intelligence; Mathematics; Radar; Statistics","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":[0.001437424,0.0007231915,0.001228244,0.0004858203,0.0005786317,0.001049809,0.001314979,0.0008057538,0.0003333169],"category_scores_gemma":[0.004867628,0.0005789224,0.0005673987,0.001077918,0.0008187992,0.002123863,0.001596944,0.0009978261,0.0001941754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004293398,"about_ca_system_score_gemma":0.0007350347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001514142,"about_ca_topic_score_gemma":0.001048144,"domain_scores_codex":[0.9988641,0.0002542329,0.0000658261,0.0003233419,0.0003713903,0.0001211813],"domain_scores_gemma":[0.9975486,0.001480647,0.0003530214,0.0002672589,0.0002908692,0.00005956032],"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":[0.0003143814,0.00005906838,0.003009511,0.0001362584,0.00006478903,0.000306314,0.0002184689,0.8220088,0.01699913,0.0114963,0.0005275591,0.1448594],"study_design_scores_gemma":[0.00001114751,0.00004109939,0.0004091658,0.000004225125,0.00001216676,0.0001255221,0.0000116337,0.9923415,0.003760604,0.002941284,0.0003311822,0.00001039268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01093279,0.00008665911,0.9885776,0.00002845374,0.00001230474,0.000005706671,0.000008876754,0.0001026322,0.000245055],"genre_scores_gemma":[0.7941614,0.0002925546,0.2037702,0.0001030966,0.00007247365,0.00005546751,0.0001052641,0.00004969962,0.001389817],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001514142,"threshold_uncertainty_score":0.007601917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009079786969141728,"score_gpt":0.2212334031559733,"score_spread":0.2121536161868316,"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."}}