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