{"id":"W2606059557","doi":"10.1016/j.jfranklin.2017.04.004","title":"Direction of arrival tracking for signals with known waveforms based on block least squares techniques","year":2017,"lang":"en","type":"article","venue":"Journal of the Franklin Institute","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Least-squares function approximation; Algorithm; Block (permutation group theory); Waveform; Sensor array; Computer science; Direction of arrival; QR decomposition; Total least squares; Tracking (education); Antenna array; Non-linear least squares; Mathematics; Estimation theory; Antenna (radio); Estimator; Statistics; Singular value decomposition; 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":[0.0007765312,0.0008220385,0.0007159205,0.0009067964,0.0003685923,0.0008342004,0.0007193287,0.0009599915,0.001228864],"category_scores_gemma":[0.005538985,0.0006135283,0.0004697422,0.001181196,0.0004112702,0.001780855,0.0007116297,0.001382046,0.001258007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003349118,"about_ca_system_score_gemma":0.0009490013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001531893,"about_ca_topic_score_gemma":0.002251179,"domain_scores_codex":[0.9995325,0.0001319407,0.00002712408,0.00006959537,0.0002085631,0.00003034195],"domain_scores_gemma":[0.9985105,0.0006623313,0.0001910524,0.0001851348,0.0004150184,0.00003585199],"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":[0.0004653616,0.0001226647,0.001434703,0.0003120657,0.0001339571,0.00009331587,0.0002323803,0.3371455,0.08624364,0.02819542,0.003280707,0.5423404],"study_design_scores_gemma":[0.00001487324,0.00005308361,0.0004138898,0.00001873958,0.00001834128,0.00007319127,0.00001575484,0.9844683,0.007968537,0.004926114,0.002011242,0.00001797086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003200252,0.0001258931,0.9961814,0.00003141698,0.00002555829,0.000007241095,0.00001274905,0.0001155322,0.0003000213],"genre_scores_gemma":[0.1089757,0.0008216692,0.8868784,0.00005606828,0.00006676184,0.00008775277,0.0002267753,0.0001275608,0.002759314],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001531893,"threshold_uncertainty_score":0.004110992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02463115717723236,"score_gpt":0.2896053300689138,"score_spread":0.2649741728916815,"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."}}