1. Detection and Estimation Theory Introduction to ECE 531
Mojtaba Soltanalian- UIC

2. The course Lectures are given Tuesdays and Thursdays, 2:00-3:15pm
Office hours: Thursdays 3:45-5:00pm, SEO 1031
Instructor:
Prof. Mojtaba Soltanalian
office: SEO 1031
web:

3. The course Course webpage: http://msol.people.uic.edu/ECE531
Textbook(s):
* Fundamentals of Statistical Signal Processing, Volume 1: Estimation Theory, by Steven M. Kay, Prentice Hall, 1993, and (possibly)
* Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory, by Steven M. Kay, Prentice Hall 1998,
available in hard copy form at the UIC Bookstore.

4. /Graduate Course with Active Participation/
The course
Style:
/Graduate Course with Active Participation/

5. Introduction
Let’s start with a radar example!

6. Introduction> Radar Example
QUIZ

7. You can actually explain it in ten seconds!
Introduction> Radar Example
You can actually explain it in ten seconds!

8. Introduction> Radar Example
Applications in Transportation, Defense, Medical Imaging, Life Sciences, Weather Prediction, Tracking & Localization

9. Introduction> Radar Example
The strongest signals leaking off our planet are radar transmissions, not television or radio. The most powerful radars, such as the one mounted on the Arecibo telescope (used to study the ionosphere and map asteroids) could be detected with a similarly sized antenna at a distance of nearly 1,000 light-years.
- Seth Shostak, SETI

10. Introduction> Estimation
Traditionally discussed in STATISTICS.
Estimation in Signal Processing:
Signal/Information Processing
ADC/DAC (Sampling)
Digital Computers

11. Introduction> Estimation
The primary focus is on obtaining optimal estimation algorithms that may be implemented on a digital computer.
We will work on digital signals/datasets which are typically samples of a continuous-time waveform.

12. Introduction> Estimation
Estimation theory deals with estimating the values of parameters based on measured/empirical data that has a random component.
The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data.
An estimator attempts to approximate the unknown parameters using the measurements.

13. Introduction> Detection
Detection theory is a means to quantify the ability to discern between information-bearing patterns and random patterns (called noise).
Typically boils down to a “hypothesis test” problem.

14. Introduction> Modeling for Detection and Estimation

15. Introduction> Estimation or Detection– which comes first?

16. Introduction> Communication Examples

17. Introduction> Communication Examples

18. Introduction> Communication Examples

19. Introduction> System Identification

20. Introduction> Clustering in Social Networks

21. Introduction> Parameter Estimation Via Sensor Networks

22. Next Lecture: Basics- A Refresher