detection and estimation theory introduction to ece 531
TRANSCRIPT
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: [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
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