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Detection and Estimation Theory

Introduction to ECE 531Mojtaba 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: msol@uic.edu

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

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 onemounted on the Arecibo telescope (used to study the ionosphere andmap asteroids) could be detected with a similarly sized antenna at adistance of nearly 1,000 light-years.

- Seth Shostak, SETI

Introduction> Radar Example

Introduction> Estimation

Traditionally discussed in STATISTICS.

Estimation in Signal Processing:

Signal/Information Processing

ADC/DAC(Sampling)

Digital Computers

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