Mon Apr 20 09:45:37 2009
| Approvals Received: |
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| Approvals Pending: | College/Dean > Catalog | |
| Effective Status: | Active | |
| Effective Term: | 1099 - Fall 2009 | |
| Course: | EE 5251 | |
| Institution: | UMNTC - Twin Cities | |
| Career: | GRAD | |
| College: | TIOT - Institute of Technology | |
| Department: | 11122 - Electrical & Computer Eng | |
| General | ||
| Course Title Short: | Optimal Filtering & Estimation | |
| Course Title Long: | Optimal Filtering & Estimation | |
| Max-Min Credits for Course: |
3.0 to 3.0 credit(s) | |
| Catalog Description: |
Basic probability theory and stochastic processes. The Gauss-Markov model. Batch/recursive least squares estimation. Filtering of linear and non-linear systems using Kalman and Extended Kalman filters. Computational aspects. Continuous-time Kalman-Bucy filter. Unscented Kalman filter and particle filters. Applications. | |
| Print in Catalog?: | Yes | |
| CCE Catalog Description: |
<no text provided> | |
| Grading Basis: | Stdnt Opt | |
| Topics Course: | No | |
| Honors Course: | No | |
| Delivery Mode(s): | Classroom | |
| Instructor Contact Hours: |
3.0 hours per week | |
| Years most frequently offered: |
Every academic year | |
| Term(s) most frequently offered: |
Fall | |
| Component 1: | LEC (with final exam) |
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| Auto-Enroll Course: |
No | |
| Graded Component: |
LEC | |
| Academic Progress Units: |
Not allowed to bypass limits. 3.0 credit(s) |
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| Financial Aid Progress Units: |
Not allowed to bypass limits. 3.0 credit(s) |
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| Repetition of Course: |
Repetition not allowed. | |
| Course Prerequisites for Catalog: |
[Math 2243, Stat 3021] or equiv, [EE 3025, EE 4231 recommended], IT grad student or % | |
| Course Equivalency: |
No course equivalencies | |
| Consent Requirement: |
No required consent | |
| Enforced Prerequisites: (course-based or non-course-based) |
000356 - IT grad student | |
| Editor Comments: | <no text provided> | |
| Proposal Changes: | <no text provided> | |
| History Information: | <no text provided> | |
| Faculty Sponsor Name: |
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| Faculty Sponsor E-mail Address: |
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| Liberal Education | ||
| Requirement this course fulfills: |
None | |
| Other requirement this course fulfills: |
None | |
| Criteria for Core Courses: |
<no text provided> | |
| Criteria for Theme Courses: |
<no text provided> | |
| Writing Intensive | ||
| Propose this course as Writing Intensive curriculum: |
No | |
| Question 1: | <no text provided> | |
| Question 2: | <no text provided> | |
| Question 3: | <no text provided> | |
| Question 4: | <no text provided> | |
| Question 5: | <no text provided> | |
| Question 6: | <no text provided> | |
| Question 7: | <no text provided> | |
| Course Syllabus | ||
| Course Syllabus: |
Optimal filtering & estimation We used to offer a course on roughly the same topics as EE5712 until about 15 years ago. We discontinued due to (from what I recall) not having enough faculty in the controls area to cover all courses that we thought essential. For the past 4 years we effectively re-instituted the course (taught it as EE 8950), cross-listed it with the Department of Aerospace & Mechanics (AEM 5451), and shared responsibility by teaching it on alternate years. Course outline of our recent course offerings 1) Fundamentals of probability theory & stochastic processes, and inear dynamical systems. 2) The Gauss-Markov model, Discrete-time Kalman filtering, computational aspects (square-root filters, and fast algorithms), optimal smoothing, Levinson, & Wiener filtering. 3) Brownian motion and stochastic differential equations, Continuous-time Kalman-Bucy filter, H1-filtering. 4) Nonlinear filtering: Extended Kalman filter, Unscented Kalman filter, Particle filters, and monte carlo methods. 5) Applications. Catalog information: AEM 5451: Optimal Estimation, 3 credits Prerequisites: [Math 2243, Stat 3021] or equiv, 4321 Topics: Basic probability theory. Batch/recursive least squares estimation. Filtering of linear and non-linear systems using Kalman and Extended Kalman Filters. Applications to sensor fusion, fault detection, and system identification. Textbook: Required: Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches, Simon, Wiley, ISBN: 0471708585 |
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