Advanced Statistical and Distributed Signal Processing

Garant předmětu:

Přednášející:

Cvičící:

Předmět zajišťuje:

katedra radioelektroniky

Anotace:

Lecturer: prof. Umberto Spagnolini (Politecnico di Milano, Italy)

Scopes are: 1) provide advanced statistical methods in signal processing following a pragmatic approach with the minimum fundamental background on estimation theory, 2) illustrate the methodology to approach some broad-interest problems.

During the first part of the course the fundamentals of statistical signal processing are reviewed, and the second part is focused to selected advanced statistical and distributed signal processing areas where the methods are paired to some applications general enough to provide a useful background for many interdisciplinary context such as audio and digital communications, imaging and machine learning, navigation and estimation in networks. After fundamentals, selected topics will be agreed with attendees to guarantee that the majority of the students could broad their cultural know-how still being focused to the area of their interest.

Požadavky:

After every lecture, there will be a written test on the topic covered. Top-grade (A) is obtained only by an in-depth discussion of one topic. Final grading/exam will be based on the homeworks/mini-projects using analytical tools from statistical signal processing to solve selected topic of student interest.

Osnova přednášek:

1.Fundamentals of estimation theory (BLUE, MLE, CRB, MMSE, MAP)

2.Parameter tracking (Kalman and particle filtering)

3.Spectral analysis (AR, MA, periodogram) and high-resolution methods for line spectra.

4.Array signal processing and multichannel MIMO processing

5.Distributed signal processing

6.Selected topics from (a) Signal detection, data classification and clustering, (b) Equalization and deconvolution, (c) Delay estimation, positioning, and navigation.

Osnova cvičení:

Cíle studia:

Studijní materiály:

U.Spagnolini, Statistical Signal Processing in Engineering, Wiley Ed. 2018

Poznámka:

předmět probíhá formou blokové výuky po dobu 4 týdnů

Další informace:

Pro tento předmět se rozvrh nepřipravuje

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