DS4111 Statistical Signal Processing Laboratory Syllabus:

DS4111 Statistical Signal Processing Laboratory Syllabus – Anna University PG Syllabus Regulation 2021

PRACTICAL EXERCISES:

Using Simulation Software Tools
1. Simulation of standard discrete time deterministic and random signals
2. Simulation of spatially separated target signal
a. In the presence of Additive Correlated White Noise
b. In the presence of Additive Uncorrelated White Noise
3. Detection of Constant Amplitude Signal, Time varying Known Signals, Unknown Signals.
4. Estimation of PSD of a noisy signal using Periodogram and Modified Periodogram.
5. Estimation of PSD using different methods (Bartlett, Welch, Blackman-Tukey).
6. Estimation of power spectrum using parametric methods (Yule Walker& Burg).
7. State Space Matrix evolution from Differential Equation
8. Normal Equation evolution Using Levinson-Durbin
9. Cascade and Parallel Realization of IIR filter
10. Implementation of Normal Density Estimation
11. Implementation of Wiener Filter for 1-D Signals
12. Implementation of LMS and RLS algorithm for the given problem
13. Estimation techniques – MLE, MMSE, Bayes Estimator, MAP Estimator
14. Implementation of Expectation Maximization (EM) algorithm
15. Performance comparison of the Estimation techniques

TOTAL:60 PERIODS

COURSE OUTCOMES:

On the successful completion of the course, students will be able to
CO1:Simulate standard discrete time signals and random signals
CO2:Detect signals in the presence of noise using appropriate method
CO3:Estimate signals and parameters using appropriate estimation techniques
CO4:Implement adaptive filtering concept for the given problem
CO5:Analyze the performance of detection and estimation techniques.