The goal is the acquisition of concepts, methods and algorithms used in statistical digital signal processing field.
1. Signal modeling: Least square method; the Padé approximation; Prony´s method; linear prediction; and stochastic models.
2. Wiener filtering: FIR and IIR filter solutions; and discrete Kalman filter.
3. Spectrum estimation: Modern estimation methods.
4. Adaptive filtering: FIR and IIR filter solutions.
Probability and Statistics; Digital Signal Processing
Generic skills to reach
. Competence in analysis and synthesis; . Competence to solve problems; . Capacity of decision; . Critical thinking; . Research skills; . Competence in understanding the language of other specialists; . Competence in applying theoretical knowledge in practice; (by decreasing order of importance)
Teaching hours per semester
total of teaching hours
Laboratory or field work
Synthesis work thesis
assessment implementation in 20202021 Assessment Synthesis work: 50.0% Exam: 50.0%
Bibliography of reference
Monson H. Hayes (1996) - Statistical Digital Signal Processing and Modeling, John Wiley.
Charles W. Therrien (1992) - Discrete Random Signals and Statistical Signal Processing, Prentice-Hall.
Laboratório computacional de processamento de sinal