Disciplina Curricular
Detecção Estimação e Filtragem (Cmu) DEF-Cmu
Diploma de Estudos Avançados em Engenharia Electrotécnica e de Computadores - DEAEEC2006
Peso
6.0 (para cálculo da média)
Objectivos
This course will introduce fundamental concepts and methods on detection, estimation, and filtering theory for signal processing and linear systems in the presence of stochastic disturbances. Students attending the course will be able to formulate and solve problems such as detection of event occurrences, extracting relevant information about the event, parameter estimation, system state estimation, sensor fusion, and dynamic smoothing. The analysis of the solutions obtained will be addressed based on concepts discussed along the course. Applications to Radar, Sonar, Speech, Image Analysis, Communications, Navigation and Control will be used to illustrate the main concepts.
Programa
Motivation for estimation and detection in stochastic signal processing; Random processes and linear systems; Detection theory: Receiver Operating Characteristics (ROC); Bayes risk; Minimum probability of error; Multiple hypothesis testing; Neyman-Pearson Theorem; Estimation theory; Characteristics of estimators; Cramer-Rao lower bound; Linear systems in the presence of stochastic signals; Best linear unbiased estimators; Maximum likelihood estimation; Deterministic and stochastic least squares; Bayesian estimation; Wiener filtering; Kalman filtering;
Metodologia de avaliação
• 13 individual homeworks (1 per week, except 1st week) with problems on the subjects taught in the previous week – 20% (average grade) • 2 mid-term exams (last week of September – covering first 5 weeks, 2nd week of November – covering weeks 6-10) and 1 final exam – covering the whole course (2nd week of December) - 20%, 20% and 40%, respectively • Final grades: o A: [85%, 100%] o B: [70%, 85%[ o C: [60%, 70%[ o D: [50%, 60%[ o F: [0, 50%[