{"id":"W4293863108","doi":"10.1109/siu55565.2022.9864811","title":"Time Resource Management in Cognitive Radars Based on Parameter Optimization","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Computer science; Radar; Kalman filter; Radar tracker; Real-time computing; Waveform; Track (disk drive); Resource management (computing); Resource (disambiguation); Simulation; Artificial intelligence; Telecommunications","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":[0.000632261,0.0006435927,0.0006862682,0.0003620043,0.0004081826,0.0009629154,0.0007133292,0.0005178892,0.0006727787],"category_scores_gemma":[0.001663415,0.0002094104,0.0002923445,0.0004742583,0.0004223506,0.001097182,0.0007035271,0.0004655758,0.0001723126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004499702,"about_ca_system_score_gemma":0.0008006434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003026128,"about_ca_topic_score_gemma":0.002237849,"domain_scores_codex":[0.999562,0.00009832151,0.00002380783,0.0001063189,0.0001254791,0.00008412034],"domain_scores_gemma":[0.9995529,0.0001887334,0.00008287294,0.00004882957,0.0001043155,0.00002245379],"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":[0.00012622,0.00007574923,0.0007790638,0.00006257986,0.00004164295,0.00008847025,0.00008764442,0.8984052,0.01192174,0.008249049,0.0004453701,0.07971717],"study_design_scores_gemma":[0.000006668723,0.00004712385,0.0002189884,0.000003243728,0.000009679754,0.00002978633,0.00001460459,0.9959105,0.001795886,0.001629718,0.0003257054,0.000008001322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05308328,0.0005471281,0.9430454,0.0001016959,0.00003335034,0.00002609953,0.00002002241,0.0002048407,0.002938139],"genre_scores_gemma":[0.9569057,0.0001959806,0.04185314,0.00004409371,0.0000243635,0.00003618541,0.00002283078,0.00002511591,0.0008925943],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003026128,"threshold_uncertainty_score":0.006017029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02182270003081152,"score_gpt":0.2471613363672647,"score_spread":0.2253386363364532,"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."}}