Detection and Estimation Theory Introduction to ECE 531 Mojtaba Soltanalian- UIC 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 email: [email protected] web: http://msol.people.uic.edu/ 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. The course

 Style:

/Graduate Course with Active Participation/ Introduction

 Let’s start with a radar example! Introduction> Radar Example

 QUIZ Introduction> Radar Example You can actually explain it in ten seconds! Introduction> Radar Example

Applications in

Transportation, Defense, Medical Imaging, Life Sciences, Weather Prediction, Tracking & Localization 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 Introduction> Estimation

 Traditionally discussed in STATISTICS.  Estimation in Signal Processing:

Digital Computers

ADC/DAC (Sampling)

Signal/Information Processing 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. 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. 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. Introduction> Modeling for Detection and Estimation Introduction> Estimation or Detection– which comes first? Introduction> Communication Examples Introduction> Communication Examples Introduction> Communication Examples Introduction> System Identification Introduction> Clustering in Social Networks Introduction> Parameter Estimation Via Sensor Networks Next Lecture: Basics- A Refresher