Course Content
1. Spectral Estimation * Non-parametric methods * Parametric methods: Line spectrum, ARMA process * Array signal processing, beamforming * CRLBs and related mathematics 2. Optimum filters: Weiner Filtering 3. Adaptive filters: Recursive LS filters 4. Kalman filter 5. Splines and sampling in shift-invariant spaces, curve fitting, and denoising 6. Introduction to compressive sensing: Identifiability, spark, OMP, ISTA 7. Sub-Nyquist Sampling: FRI, multiband signals, and applications 8. Filter banks and multi-rate systems 9. Two-dimensional signals, Fourier transform, sampling 10. Model-based machine learning
Text / References
- 1 Monson H. Hayes, “Statistical signal processing and modeling”, Wiley India Pvt. Ltd., 2002. (Indian edition available)
- 2 Petre Stoica and Randolph Moses, “Spectral analysis of signals”, Prentice Hall, 2005. (Indian edition available)
- 3 Charles W. Therrien, “Discrete random signals and statistical signal processing”, Charles W. Therrien, 2004.
- 4 Research publications from journals.