{"id":"W2294783111","doi":"10.1109/taes.2015.140574","title":"A practical bias estimation algorithm for multisensor-multitarget tracking","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":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; McMaster University","funders":"","keywords":"Fusion center; Computer science; Filter (signal processing); Tracking (education); Sensor fusion; Algorithm; Transmission (telecommunications); Sampling (signal processing); Tracking system; Radar tracker; Real-time computing; Artificial intelligence; Computer vision; Radar; Telecommunications; Wireless","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":[],"consensus_categories":[],"category_scores_codex":[0.0004823758,0.0002464489,0.0002736772,0.0001241689,0.0003945064,0.0002555025,0.0002188085,0.0001729698,0.0000067577],"category_scores_gemma":[0.00002990365,0.0001804735,0.0001056926,0.0002232218,0.00005449758,0.0005556078,0.000002799251,0.0002373876,0.00004143064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001388809,"about_ca_system_score_gemma":0.00009628139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006057203,"about_ca_topic_score_gemma":0.00003091413,"domain_scores_codex":[0.9979968,0.0001217422,0.0003323761,0.0006033882,0.0002889636,0.0006567105],"domain_scores_gemma":[0.9982248,0.0008983021,0.0001384095,0.0004645533,0.0001193781,0.0001545786],"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":[0.00008235432,0.0003281501,0.00001889307,0.00005813298,0.0001462439,0.00001168343,0.0003444811,0.01028142,0.006564089,0.01060958,0.004071204,0.9674838],"study_design_scores_gemma":[0.001686094,0.0004625344,0.00002354742,0.000176503,0.00003905849,0.0001948835,0.00006409669,0.9786801,0.008841373,0.0002123133,0.009222084,0.0003973595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002115618,0.0002214502,0.9940805,0.001497325,0.001173248,0.0005559423,0.00004765565,0.0002881471,0.00002005473],"genre_scores_gemma":[0.9027073,0.0003558986,0.09579565,0.0000835312,0.0001332407,0.000176631,0.000003088701,0.00003109887,0.0007135788],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9683987,"threshold_uncertainty_score":0.7359496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03823993947948046,"score_gpt":0.2898215399782648,"score_spread":0.2515816004987843,"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."}}