Signal Theory

The course "Signal Theory" will provide a deeper look into stochastic and deterministic signals, in particular their characterization and estimation. Signals with specific structure, in particular topics in sampling and reconstruction and Compressed Sensing are covered.

Random Variables and Estimation

• Random Variables
• Estimation

Random Processes and Estimation

• Stochastic Processes
• Mean-Square Estimation and Ergodicity

Signals with Structure and Reconstruction / Estimation

• Sampling and Interpolation
• Compressed Sensing

Organization

The lecture and the related exercises are organized via Moodle.
Please register yourself in the moodle course, if you are interested in participating in this lecture.

The lecture starts on Tuesday, November 10, 8:30, in the BBB-room in Moodle.

The first exercise will be given on Monday, November 16, 10:00.

Stochastic Processes / Detection

• A. Papoulis, S.U. Pillai. Probability, Random Variables and Stochastic Processes. McGraw-Hill, New York, 4th edition, 2002
• H.L. Van Trees. Detection, Estimation, and Modulation Theory, Part III: Radar-Sonar Signal Processing and Gaussian Signals in Noise. John Wiley & Sons Inc., 2001.
• R.G. Gallager. Stochastic Processes: Theory for Applications. Cambridge University Press, 2013.
• B. Hajek. Random Processes for Engineers. Cambridge University Press, 2015.

Digital Signal Processing / Compressed Sensing

• M. Vetterli, J. Kovacevic, V.K. Goyal. Foundations of Signal Prosessing. Cambridge University Press, 2014.
• Y.C. Eldar. Sampling Theory: Beyond Bandlimited Systems. Cambridge University Press, 2015.
• Y.C. Eldar, G. Kutyniok (editors). Compressed Sensing: Theory and Applications. Cambridge University Press, 2012.
• K.-D. Kammeyer, K. Kroschel. Digitale Signalverarbeitung: Filterung und Spektralanalyse. Springer Vieweg, 8. Auflage, 2012.