IT 888/ECE 753/SYST 684: DISTRIBUTED ESTIMATION AND MULTISENSOR

TRACKING AND FUSION

Fall 2004

Instructor: Dr. K. C. Chang Class room: SITE II 320

Class time: Mon 7:20 – 10:00 PM Office hours: Mon 3:00 ~ 5:00 PM

Office phone: 993-1639 Office no.: SITE-II: 315

Unit 1	Unit 2	Unit 3	Unit 4	Unit 5	Unit 6
Unit 7	Unit 8	Unit 9	Unit 10	Unit 11	Unit 12

Homework 1	Homework 2	Homework 3	Homework 4	Homework 5
Solution 1	Solution 2	Solution 3	Solution 4	Solution 5

Course Schedule

8/30 - Unit #1, 9/6 - Labor day recess, 9/13 - Unit#2, 9/20 - Unit#3, 9/27 - Unit #4, 10/4 – Unit #5, Project proposal due,

10/12 – Unit #6 (Not 10/11, Columbus day), 10/18 – Mid-term, 10/25 – Unit #7, 11/1 – Unit #8, 11/8 – Unit #9,

11/15 – Unit #10, 11/22 – Unit #11, 11/29 – Unit #12/Project presentation, 12/6 – Project presentation,

12/13 – Final Exam, Project due

Course Description

Centralized and distributed estimation theory, hierarchical estimation, tracking and data association, multisensor multitarget tracking and fusion, distributed tracking in distributed sensor networks, track-to-track association and fusion, Bayesian networks for multisensor fusion.

Prerequisites: ECE 528 or SYST 611

Course Objectives

The main objective of the course is to introduce students to advanced topics in distributed estimation and multisensor multitarget tracking and fusion. Students will study different data association and tracking algorithms ranging from single maneuvering target to multiple targets under clutter environments. Both centralized as well as the distributed version of the algorithms will be covered. The distributed framework where data and tracks are fused from multiple sensor/processors will be studied. The issues and methodologies of applying Bayesian networks for data fusion will also be discussed.

Course Outline

1. Course overview. Review of important concepts in estimation and multisensor tracking and fusion

2. Centralized and distributed estimation, Hierarchical estimation theory

3. Tracking with Probabilistic Data Association filter (PDAF, JPDAF)

4. Interactive multiple model algorithms (IMM, IMMPDA)

5. Distributed IMM and PDA algorithms

6. Multiple hypothesis tracker (MHT)

7. Multisensor track-to-track association and fusion.

8. Distributed tracking in distributed sensor networks (DSN).

9. Bayesian Networks representation and algorithms

10. Bayesnet for multisensor data fusion

11. Performance evaluation multisensor tracking and fusion

Course Assignments and Grading

There will be several homework assignments and a project assignment. There will be a mid term, and a final exam, both take-home. They will constitute 25%, 20%, 25%, and 30% of the grade, respectively.

Course Materials

Proposed Texts

1. Y. Bar-Shalom and X. Li, Multitarget-Multisensor Tracking: Principles and Techniques, YBS Publishing, 1995.

2. Y. Bar-Shalom, X. Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation, John Wiley, 2001.

Supplementary Texts

1. S. S. Blackman, Multiple Target Tracking with Radar Applications, Artech House, 1986.

2. E. Waltz and J. Llinas, Multisensor Data Fusion, Artech House, 1990

3. Y. Bar-Shalom, Multitarget multisensor tracking : Applications and Advances, Vol. I and II, Academic Press, 1990, 1992.

4. S. S. Blackman and Robert Popoli, Design and Analysis of Modern Tracking Systems, Artech House, 1999.

5. Y. Bar-Shalom and Dale Blair, Multitarget multisensor tracking : Applications and Advances, Vol. III, Artech House, 2000.

Papers

1. J. L. Speyer, "Computation and Transmission Requirements for a ecentralized Linear-Quadratic-Gaussian Control Problem," IEEE Trans. on Automat. Contr., Vol. AC-24, April 1979.

2. C. Y. Chong, “Hierarchical Estimation,” Proc. MIT/ONR Workshop on C³, 1979.

3. D. B. Reid, "An Algorithm for Tracking Multiple Targets,'' IEEE Trans. on Automat. Contr., Vol. AC-24, pp. 843 - 854, Dec. 1979.

4. A. S. Willsky, et. al., "Combining and Updating of Local Estimates and Regional Maps along Sets of One-Dimensional Tracks," IEEE Trans. on Automat. Contr., Vol. AC-27, August 1982.

5. B. C. Levy, et. al., "A Scattering Framework for Decentralized Estimation Problem," Automatica, July 1983.

6. D. A. Castanon and Teneketzis, "Distributed Estimation Algorithms for Nonlinear Systems," IEEE Trans. on Automat. Contr., Vol. AC-30, May 1985.

7. S. Mori, C. Y. Chong, E. Tse, and R. P. Wishner, ``Tracking and Classifying Multiple Targets without A Priori Identification,'' IEEE Trans. on Automat. Contr., Vol. AC-31, No. 5, pp. 401 - 409, May 1986.

8. K. C. Chang, C. Y. Chong, and Y. Bar-Shalom, ``Distributed Estimation in Distributed Sensor Networks,'' Large-Scale Stochastic Systems Detection, Estimation, Stability and Control, Chapter 2, Marcel Dekker, 1992.

9. S. Mori, K. C. Chang, and C. Y. Chong, ``Performance Analysis of Optimal Data Association with Application to Multiple Target Tracking,'' Multitarget-Multisensor Tracking: Applications and Advances, Vol. II, Chapter 7, Artech House, 1992.

10. C. Y. Chong, ``Distributed Architecture for Data Fusion,'' Proc. Fusion’98, Las Vegas, July 1998.

11. K. C. Chang, Zhi Tian, and R. K. Saha, ``Performance Evaluation of Track Fusion with Information Filter,'' Proc. Fusion’98, Las Vegas, July 1998.

12. David Hall, and Amulya K. Garga, ``Pitfalls in Data Fusion,'' Proc. Fusion’99, Sunnyvale, July 1999.

13. Oliver E. Drummond, ``On Features and Attributes in Multisensor Multitarget Tracking,'' Proc. Fusion’99, Sunnyvale, July 1999.

14. C.Y. Chong, S. Mori, W.H. Barker, and K.C. Chang, ``Architectures and Algorithms for Track Association and Fusion,'' Proc. Fusion’99, Sunnyvale, July 1999.

15. K.C. Chang, “Evaluating Hierarchical Track Fusion with Information Matrix Filter,” Proc. Fusion’00, Paris, France, 2000.

16. X. Rong Li, Y. Zhu and C. Han, “Unified Optimal Linear Estimation Fusion – Part I: Unified Models and Fusion Results,” Proc. Fusion’00, Paris, France, 2000.

17. X. Rong Li and Keshu Zhang, “Optimal Linear Estimation Fusion – Part IV: Optimality and Efficiency of Distributed Fusion,” Proc. Fusion’01, Montreal, August, 2001.

18. C. Y. Chong and Shozo Mori, “Convex Combination and Covariance Intersection Algorithms in Distributed Fusion,” Proc. Fusion’01, Montreal, August, 2001.