{"id":"W2974697617","doi":"10.1109/taes.2019.2942411","title":"Unimodular Waveform Design With Desired Ambiguity Function for Cognitive Radar","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; University of Calgary","funders":"Defence Research and Development Canada","keywords":"Unimodular matrix; Ambiguity function; Waveform; Algorithm; Gradient descent; Mathematical optimization; Benchmark (surveying); Radar; Computer science; Optimization problem; Ambiguity; Constraint (computer-aided design); Mathematics; Artificial intelligence; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000465948,0.000604876,0.0004124618,0.0002588493,0.0001641866,0.0006147265,0.0005059408,0.0005108478,0.001128585],"category_scores_gemma":[0.00127652,0.0002006612,0.0003073408,0.0004005706,0.000435051,0.0007182632,0.0006966522,0.0006730603,0.0004442669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002201376,"about_ca_system_score_gemma":0.0006428791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003086492,"about_ca_topic_score_gemma":0.0004504678,"domain_scores_codex":[0.9997404,0.00007716123,0.00001078524,0.00005280579,0.00008906828,0.0000298618],"domain_scores_gemma":[0.9996943,0.0001365308,0.00005199796,0.0000371387,0.0000593039,0.00002071273],"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":[0.0001927313,0.00008938382,0.0004686438,0.0002971178,0.00006023255,0.0001810959,0.0001467806,0.493019,0.08197953,0.07599324,0.002619945,0.3449524],"study_design_scores_gemma":[0.00002154036,0.0001786447,0.0001451469,0.00001286165,0.00001328144,0.0001460986,0.00002614871,0.9691338,0.01047297,0.01690823,0.002927486,0.00001385254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00546754,0.0001544673,0.9930119,0.00004651889,0.00001552085,0.000008334,0.00001123522,0.00005170893,0.001232824],"genre_scores_gemma":[0.4069536,0.0005637725,0.5891707,0.0002104805,0.00009126923,0.00009458928,0.0000965307,0.00006314262,0.002755999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001128585,"threshold_uncertainty_score":0.003775477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01007238957846159,"score_gpt":0.1878262997522036,"score_spread":0.177753910173742,"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."}}