Advanced Topics in Signal Processing and Communication

Lecturer: Prof. Dr.-Ing. Jens-Rainer Ohm

Lecture index

  • Characterization of random signals, formulation of detection and estimation problems under noise and variations, higher-order statistics
  • Statistical similarity and modeling
  • Methods of signal and parameter estimation: Least squares and SVD methods, Wiener filter and linear prediction, Bayes estimation, maximum-likelihood estimation, robust estimaton
  • Orthogonality and correlation analysis, orthogonal transforms
  • Amplitude/phase relationships, Hilbert transform
  • Signal and parameter spaces, partitioning methods
  • Frequency and scale spaces, combined time/frequency analysis, multi-rate and multi-resolution sampling, filterbanks and wavelet transform
  • Extension of sampling and systems theory for multiple dimensions
  • Non-uniform sampling
  • Application examples in communication systems, signal analysis and systems optimization

Educational objective: Students shall acquire an advanced knowledge about signal processing, time/frequency characterization, sampling, estimation and detection problems with emphasis on application in communication systems signal analysis and systems optimization.

Requirement: Knowledge about fundamentals of signal processing, statistics and communication systems

Further information: RWTHonline