{"id":"W1567156622","doi":"10.1109/icassp.2015.7178733","title":"General solution and approximate implementation of the multisensor multitarget CPHD filter","year":2015,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; McGill University","funders":"","keywords":"Filter (signal processing); Computer science; Tracking (education); Set (abstract data type); Algorithm; Finite set; Simple (philosophy); Mathematics; Computer vision","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":[0.001248527,0.0003409027,0.0005992685,0.0003222282,0.0002994536,0.0007331032,0.0009381794,0.001134172,0.00235412],"category_scores_gemma":[0.003385894,0.0002912973,0.000503336,0.0004597628,0.0005896774,0.001117978,0.0008555239,0.0008573115,0.0003776966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008347387,"about_ca_system_score_gemma":0.001182037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00301769,"about_ca_topic_score_gemma":0.002164437,"domain_scores_codex":[0.9994794,0.000140334,0.00003602606,0.00009758906,0.0001961814,0.00005050316],"domain_scores_gemma":[0.99903,0.0005787005,0.00006464859,0.0001223241,0.0001794456,0.00002486706],"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":[0.00006734968,0.00002432307,0.0004564381,0.00007542338,0.00001887704,0.00007513807,0.00007720265,0.8748083,0.005448285,0.0559994,0.0006338866,0.06231545],"study_design_scores_gemma":[0.000004783684,0.000007285397,0.00003977739,0.000002255447,0.000001372423,0.00001750098,0.000004071302,0.9951736,0.0008255271,0.003629994,0.0002905583,0.000003268769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002698665,0.00003022863,0.9965765,0.00004441647,0.000007484779,0.00001008085,0.00001549958,0.00006409093,0.0005530448],"genre_scores_gemma":[0.3245822,0.0001688282,0.6721488,0.00008914302,0.00002654036,0.0001869911,0.0001191873,0.00003712751,0.002641153],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00301769,"threshold_uncertainty_score":0.007875323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03416889421215254,"score_gpt":0.2800795284588744,"score_spread":0.2459106342467219,"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."}}