This course provides an introduction to the theory and algorithms of stochastic signals and their applications to the real world. Discrete random variables, random vectors, stochastic processes, and random fields are introduced followed by frequently encountered random processes including the white noise, Gaussian processes, Markov processes, Poisson processes, and Markov random fields. Current methods used in statistical signal processing including detection, estimation, and optimal filtering, are covered. Special emphasis is given to Wiener and Kalman filters. Advanced topics in modern statistical signal processing such as linear prediction, linear models and spectrum estimation are also discussed.

Hayes, M. H., Statistical Digital Signal Processing and Modeling, John Wiley & Sons, 1996. ISBN 0-471-59431-8.

1. H. Stark and J. W. Woods, Probability and Random Processes with Applications to Signal Processing, NJ: Prentice Hall, Third edition, 2002. ISBN # 0-13-020071-9.
2. Papoulis, A., Probability, Random Variables, and Stochastic Processes, NcGraw Hill, 1991.
3. Manolakis, D. G., Ingle, V. K., and Kogon, S. M., Statistical and Adaptive Signal Processing, McGraw Hill, 2000.
4. Scharf, L. L., Statistical Signal Processing, Detection, Estimation, and Time Series Analysis, Addison-Wesley, 1990.
5. Van Trees, H. L., Detection, Estimation, and Modulation Theory, Part I, John Wiley & Sons, 2001.
6. Kay, S. M., Fundamentals of Signal Processing, Volume I: Estimation Theory, Prentice Hall, 1993.
7. Moon, T. D., Stirling, W. C., and Sterling, W. C., Mathematical Models and Algorithms for Signal Processing, Prentice Hall, 1999.
8. Therrien, C. W., Discrete Random Signals and Statistical Signal Processing, Prentice Hall, 1992.

In case of any broken links or errors, please notify Dr. Amir Asif (asif@cse.yorku.ca) through an email.

• Mondays, 5:30pm - 7:00pm, SC 212
• Wednesdays, 5:30pm - 7:00pm, SC 212
• Office Hours: Mondays, 4:00pm - 5:00pm, CSEB3028

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