Statistical Signal Processing (PhD)

The first part of the course reviews the fundamentals of statistical signal processing, the second part is focused to selected 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 (tot.24h).

  • Review of basics (matrix analysis, statistics and time series properties, constrained optimization)
  • Fundamentals of estimation theory (BLUE, MLE, CRB, MMSE, MAP)
  • Parameter tracking and Kalman filtering
  • Spectral analysis and high-resolution methods for line spectra
  • Classification, clustering, and neural networks

One or two topics selected from (all topics are covered by the book):

  • Distributed estimation
  • Equalization and deconvolution
  • Array signal processing and multichannel MIMO systems
  • Delay estimation, positioning, and navigation