DIPARTIMENTO DI INGEGNERIA INDUSTRIALE E DELL'INFORMAZIONE
Corso di studio
Space Communication and Sensing
Primo Semestre (30/09/2019 - 20/01/2020)
50 ore di attività frontale
SCRITTO E ORALE CONGIUNTI
Basic concepts in analog signal processing, spectral analysis and filtering.
Developing a strong working knowledge on signal processing algorithms for modeling discrete-time signals, designing optimum digital filters, estimating the power spectrum of a random signal, and designing and implementing adaptive filters.
Ability to implement the studied algorithms in Matlab standalone and hardware-oriented applications.
Programma e contenuti
Introduction to digital signal theory.
Discrete time signals, sampling theorem, linear shift invariant digital systems.
Analysis of digital systems in the Fourier and Z transform domains.
Discrete-time random processes.
Digital filtering of deterministic and stochastic signals.
Deterministic and stochastic signal modeling.
Wiener Filter: linear prediction, white noise filtering, unwanted signal canceling.
Adaptive filtering: LMS, RLS and Kalman algorithms.
Application examples in Matlab and programmable hardware platforms.
The course is based on lectures, practical exercises, case studies, and project examples, aimed at describing applications of statistical digital signal processing to practical utility projects.
Lectures (hours/year in lecture theatre): 44
Practicals / Workshops (hours/year in lecture theatre): 8
Testi di riferimento
Monson H. Hayes Statistical Digital Signal Processing and Modeling. John Wiley & Sons Inc, 1996.
Modalità verifica apprendimento
The exam consists of an oral test during which questions will be asked on two/three different topics regarding the main course objectives, i.e., signal modeling, adaptive filtering, and spectrum estimation, in order to cover most of the course topics.
Alternatively, each student can choose to implement a laboratory project, assigned by the teacher, followed by an in-depth interview. The assigned projects will cover most of the course topics.
The final mark is in thirtieths.
Obiettivi Agenda 2030 per lo sviluppo sostenibile