{"id":"W2163906842","doi":"10.1049/iet-spr.2009.0082","title":"Focusing inverse synthetic aperture radar images with higher-order motion error using the adaptive joint-time–frequency algorithm optimised with the genetic algorithm and the particle swarm optimisation algorithm – comparison and results","year":2010,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; Department of National Defence","funders":"","keywords":"Algorithm; Inverse synthetic aperture radar; Particle swarm optimization; Computer science; Synthetic aperture radar; Focus (optics); Genetic algorithm; Search algorithm; Radar; Radar imaging; Computer vision; Optics; Physics","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":[0.0007186802,0.0003595194,0.0004517951,0.0003737513,0.0001290489,0.0003616592,0.0003060254,0.0005424434,0.0007030251],"category_scores_gemma":[0.001118431,0.0001531978,0.0003375227,0.0003656883,0.0002681879,0.0004334325,0.0001834524,0.0002569748,0.0001291271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003236259,"about_ca_system_score_gemma":0.0003319207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00112097,"about_ca_topic_score_gemma":0.001198634,"domain_scores_codex":[0.9997786,0.00003900657,0.000009298819,0.00002441618,0.000131525,0.00001719713],"domain_scores_gemma":[0.9995192,0.000250916,0.00004461889,0.00003895404,0.0001298428,0.0000164248],"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":[0.0005546699,0.0002257536,0.00439408,0.0002734799,0.0001438584,0.0002058584,0.0002866775,0.4301734,0.2384401,0.003874166,0.0007331314,0.3206948],"study_design_scores_gemma":[0.00005525916,0.0003371686,0.004141709,0.00001357805,0.0000426385,0.0002171587,0.00004220892,0.8990379,0.09385041,0.0004815668,0.001750697,0.00002971547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3949685,0.0004273754,0.5976116,0.0001338559,0.00004328664,0.0001094675,0.0000290436,0.0005127292,0.006164115],"genre_scores_gemma":[0.5741898,0.0001969085,0.423159,0.00003432877,0.00001114747,0.00007026121,0.0000519239,0.00004096924,0.002245702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00112097,"threshold_uncertainty_score":0.003800809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01498215826125679,"score_gpt":0.2366985705253991,"score_spread":0.2217164122641423,"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."}}