UNIT I DISCRETE RANDOM SIGNAL PROCESSING

PART A

1. Define discrete random process.

2. When a random process is called as wide-sense stationary?

3. State Wiener – Khintchine relation.

4. State spectral factorization theorem.

5. Write down the properties of regular process.

6. When a wide-sense stationary process is said to be white noise?

7. Write autocorrelation and autocovariance matrices.

8. Write the properties of autocorrelation matrix.

9. What are Ensemble averages?

10. For given two random processes, x(n) and y(n), define cross-covariance and cross-correlation.

11. When two random processes are said to be orthogonal?

12. Write the properties of Wide Sense Stationary.

13. Write the power spectrum of a WSS process filtered with linear shift-invariant filter.

14. What is a regular process?

15. Write Autocorrelation of a Sum of Processes.




PART B


1. Derive the power spectral density of the process. (10)

2. State and prove Parseval’s theorem. (8)

3. Explain Autocorrelation and Autocovariance matrices.Also explain its properties.(10)

4. Explain in detail about parameter estimation: Bias and Consistency. (10)

5. Explain Spectral factorization. What is regular process? State the properties of regular

process. (12)

6. Explain filtering random processes. (12)