{"id":"W1993679774","doi":"10.1117/12.779598","title":"Passive tracking with sensors of opportunity using passive coherent location","year":2008,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; McMaster University","funders":"","keywords":"Kalman filter; Observability; Computer science; Tracking (education); Radar tracker; Extended Kalman filter; Passive radar; Real-time computing; Filter (signal processing); Bearing (navigation); Radar; Radar engineering details; Computer vision; Artificial intelligence; Telecommunications; Radar imaging","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003588837,0.0003168435,0.0004585265,0.0001233663,0.0001663762,0.00008881649,0.001253743,0.0001783246,0.000007138622],"category_scores_gemma":[0.0004304858,0.0002509662,0.0003519285,0.0005951272,0.0003439119,0.0009155646,0.0002257091,0.0003502075,5.745852e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001358009,"about_ca_system_score_gemma":0.0001089618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000249667,"about_ca_topic_score_gemma":1.895606e-7,"domain_scores_codex":[0.9974709,4.646782e-8,0.0007573343,0.0004608015,0.0009228045,0.0003881006],"domain_scores_gemma":[0.9954679,0.0001995907,0.0007963441,0.000120706,0.003241646,0.0001738321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001973119,0.0004807819,0.002185264,0.0008183105,0.0008206076,0.000002209628,0.001700495,0.01296944,0.3290891,0.6455061,0.003870624,0.002359739],"study_design_scores_gemma":[0.002524884,0.0009397583,0.007065628,0.001580605,0.0002804244,0.0003002452,0.002483952,0.6664459,0.3140102,0.001689254,0.001607713,0.001071439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930314,0.00005056518,0.004854967,0.0008666245,0.0002373914,0.0004164664,0.00003037159,0.00009761862,0.0004145562],"genre_scores_gemma":[0.8020175,0.00007650405,0.1975182,0.00005822562,0.0002074871,0.00003437302,0.00000779877,0.00003575893,0.0000441661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6534765,"threshold_uncertainty_score":0.9999943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02486674711007835,"score_gpt":0.2358629825187269,"score_spread":0.2109962354086486,"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."}}