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525.728 - Detection and Estimation Theory Course Homepage

Instructor Information

Amit Banerjee

Email: amit.banerjee@jhuapl.edu
Work Phone: (240) 228-4154

Course Information

Course Description

Both hypothesis testing and estimation theory are covered. The course starts with a review of probability distributions, multivariate Gaussians, and the central limit theorem. Hypothesis testing areas include simple and composite hypotheses and binary and multiple hypotheses. In estimation theory, maximum likelihood estimates and Bayes estimates are discussed. Practical problems in radar and communications are used as examples throughout the course.

Prerequisites

525.414 Probability and Stochastic Processes for Engineers or equivalent.

Course Goal

The subject of signal detection and estimation is concerened with the processing of information-bearing signals for the purpose of making inferences about the information that they contain.  The purpose of this course is to provide an introduction to the fundamental theoretical principles underlying the development and analysis of techniques for such processing.  This course is generally a first year graduate level course for students interested in signal processing, communications, control systems, computer science and related fields.

Course Objectives

  • Use classical and Bayesian approaches to formulate and solve problems for parameter estimation from noisy signals.

  • Use hypothesis testing and Bayesian approaches to formulate and solve problems for signal detection from noisy signals.
  • Derive and apply linear filtering methods for parameter estimation and signal smoothing.

When This Course is Typically Offered

This course is typically offered in the spring semester at APL.

Syllabus

Topics Covered

  • Review of random processes and Gaussian variables
  • Estimation theory, sufficient statistics
  • ML estimation, Bayes estimation
  • Properties of estimators, performance bounds
  • Linear filtering, sequential estimation
  • Statistical hypothesis testing
  • Bayesian, minimax, and Neyman-Pearson optimal tests

Student Assessment Criteria

Homework (5 assignments) 30%
Mid-term Exam 35%
Final Exam (take-home) 35%

All homework is due within one week of its assignment. Late homework will not be accepted without the prior permission of the instructor.

Computer and Technical Requirements

List software that may be required.
PDF Viewer:

You will need the free Adobe PDF viewer software to view PDF files in this course. Go to http://www.adobe.com/products/acrobat/readstep.html

Software: Several Matlab projects will be assigned as part of the homework assignments.

Participation Expectations

Provide a detailed list of student requirements.

This course will consist of four basic student requirements:

  1. Homework - Each student is required to complete all homework assignments to earn a course grade. Homework assignments will be evaluated and graded on a scale of 0 to 100. It is important to note that homework assignments that fully meet all objectives will receive a grade of 95. The remaining 5 points are reserved for products that go beyond the established objectives of the assignment and clearly identify additional effort, additional research, or self-assessment. Homework not submitted will receive a grade of 0, resulting in an incomplete for the course. Late homework assignments will be reduced by a 10 point penalty per week late. Deficient homework will be returned to students for resubmission within 1 week; the final grade for the specific homework assigned will be the average of the two grades. The intent is to ensure that you are successfully learning the concepts taught in this course. Homework assignments will generally involve answering a scenario problem based upon a specific project management skill. Homework assignments will be uploaded into the course site and will be accepted in ASCII or Microsoft Word 97(R) (or later) format. Any resubmissions should be sent to the grading instructor* via the course site mail.
    *The grading instructor is listed in the Course Outline.

  2. Examinations - A mid-term and a final will be given. Each will be graded on a scale of 0 to 100. Exact dates will be announced later during the semester.

Textbooks

Textbook information for this course is available online through the MBS Direct Virtual Bookstore.

Course Notes

There are no notes for this course.

(Last Modified: 08-10-2009 at 1:29:28 PM)