EEweb Home     ::     Graduate Courses     ::     Undergraduate Courses     ::     My Home

Course Description Form

Course number and title: EE230A Estimation and Detection in Communication and Radar Engineering
Credits: 4
Instructor(s)-in-charge: K. Yao (yao@ee.ucla.edu)
Course type: Lecture
Required or Elective: A Signals and Systems course.
Course Schedule:
Lecture: 4 hrs/week.
Dicussion: 1 hr/week.
Outside Study: 7 hrs/week.
 
Course Assessment:
Homework: About 8 homework assignments.
Exams: 1 midterm and 1 final examination.
 
Grading Policy: Typically, 20% homework assignments, 30% midterm, 50% final.
Course Prerequisites: EE131A.
Catalog Description: Applications of estimation and detection concepts in communication and radar engineering; random signal and noise characterizations by analytical and simulation methods; mean square (MS) and maximum likelihood (ML) estimation and algorithms; detection under ML, Bayes, and Neyman/Pearson (NP) criteria; signal-to-noise ratio (SNR) and error probability evaluations.  
Textbook and any related course material:
R.N. McDonough and A.D. Whalen, Detection of Signals in Noise, 2nd edition, Academic Press, 1995.
 
Course Website
Topics covered in the course:
Review of basic probability concepts including multidimensional Gaussian pdf and wide-sense stationary random processes.
Hypothesis testing: Bayes criterion, Minimax criterion, Neyman-Pearson criterion, and likelihood ratio test
Detection of deterministic vector/waveform in white/colored Gaussian noise vectors and processes.
Coherent detection, matched filtering, and correlation receiver.
Detection of signals with random parameters and non-coherent detection.
M-ary detection and geometric interpretation of signal spaces.
Evaluation of error probability of various digital communication systems.
Introduction to parameter estimation: Bayes estimator; ML estimator; MAP estimation; sufficiency; CR bound.
Minimum linear mean-square estimation and orthogonal principle.
Selected examples in digital communication and radar systems illustrating detection and estimation principles.
Will this course involve computer assignments? NO Will this course have TA(s) when it is offered? NO

:: Last modified: September 2007 by K. Yao ::

Copyright © 2003 UCLA Electrical Engineering Department. All rights reserved.
Please contact eeweb@ee.ucla.edu for comments or questions for the website.