SGN-21006 Advanced Signal Processing, 5 cr
Vastuuhenkilö
Ioan Tabus
Opetus
| Toteutuskerta | Periodi | Vastuuhenkilö | Suoritusvaatimukset |
| SGN-21006 2016-01 | 1 - 2 |
Ioan Tabus |
Final examinaion and a homework assignment |
Osaamistavoitteet
Student will learn advanced signal processing methods, especially linear optimal filter design, adaptive filters, spectrum estimation, nonlinear filters and how to select proper methods for signal processing tasks at hand. After completing the course, the student - Is familiar with the most important advanced signal processingr generic problems: optimal design, convergence, recursiveness in time, spectrum estimation; - Is able to start from the formulation of a problem formulation and utilize a number of typical algorithmic tools to derive the solution; - Knows what are the most important salgorithms for optimal and adaptive filters: LMS, NLMS,RLS etc. - Acquires practice on simulating optimal and adaptive algorithms with given input data and extracting useful performance indices helpful in comparing various algorithms. - Knows how to integrate an optimal or adaptive filter in a number of important applications: echo cancelation, noise cancellation, channel equalization etc.
Sisältö
| Sisältö | Ydinsisältö | Täydentävä tietämys | Erityistietämys |
| 1. | 1. Deterministic and random signals: review of Fourier transform, Z transform, random variables, random signals, correlation, AR,MA, ARMA | ||
| 2. | 2. Optimal filter design (Wiener filter, Least squares, essentials of estimation, MLE, CramerRao) | ||
| 3. | 3. Adaptive filter design (LMS, NLMS, RLS, Kalman ) | ||
| 4. | 4. Application areas of Optimal filter design and Adaptive filter design | ||
| 5. | 5. Spectrum estimation:Frequency spectrum (needed in machine function regime diagnosis, finding periodicities in time series), Direction of Arrival spectrum | ||
| 6. | 6. Nonlinear filters (median and order statistics filter family) |
Ohjeita opiskelijalle osaamisen tasojen saavuttamiseksi
The course is graded on the basis of answers to exam questions. Very good grade is obtained when exam questions are correctly answered and homework is accepted. Course acceptance threshold is approx. half of the maximum exam points. By volunteering to show exersice solution will be rewarded with increasing the exam result by one grade if the threshold is passed.
Arvosteluasteikko:
Numerical evaluation scale (0-5)
Oppimateriaali
| Tyyppi | Nimi | Tekijä | ISBN | URL | Lisätiedot | Tenttimateriaali |
| Book | Adaptive Filter Theory | Simon O. Haykin | No | |||
| Book | Optimum Signal Processing | S. J. Orfanidis | No | |||
| Book | Spectral analysis of signals | Petre Stoica and Randolph Moses | No | |||
| Lecture slides | Ioan Tabus | Yes |
Esitietovaatimukset
| Opintojakso | P/S | Selite |
| SGN-11000 Signaalinkäsittelyn perusteet | Advisable | |
| SGN-11006 Basic Course in Signal Processing | Advisable |
Vastaavuudet
| Opintojakso | Vastaa opintojaksoa | Selite |
| SGN-21006 Advanced Signal Processing, 5 cr | SGN-2607 Statistical Signal Processing, 6 cr |