Metric selection for information theoretic sensor management

Proceedings of the 11th International Conference on Information Fusion, FUSION 2008

Volumn , Issue , 2008, Pages

  Aughenbaugh, Jason Matthew ^a   La Cour, Brian R ^a  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

Divergence; Entropy; Kullback Leibler; Mutual information; R nyi; Sensor management

Indexed keywords

BAYESIAN NETWORKS; FUSION REACTIONS; INFORMATION FUSION; INFORMATION THEORY; RHENIUM; SENSOR DATA FUSION; SENSORS;

BAYESIAN; BAYESIAN UPDATING; DIFFERENTIAL ENTROPIES; DIVERGENCE; KULLBACK-LEIBLER; KULLBACK-LEIBLER DIVERGENCES; MUTUAL INFORMATION; MUTUAL INFORMATIONS; PARAMETER VALUES; PARTICLE FILTERS; PROBLEM INVOLVES; SENSOR MANAGEMENT; SENSOR MANAGEMENTS; TIME- DELAYS; TRACKING PROBLEMS;

DATA FUSION;

EID: 56749143778     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIF.2008.4632451     Document Type: Conference Paper

References (23)

1. D. T. Gillespie. Exact Numerical Simulation of the Ornstein-Uhlenbeck Process and Its Integral. Physical Review E, Vol. 54, No. 2, pp. 2084-2091, 1996.
2. L. D. Stone, C. A. Barlow, and T. L. Corwin, Bayesian Multiple Target Tracking, Artech House, Boston, 1999.
3. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing, Vol. 50, No. 2, pp. 174-188, 2002.
4. W. R. Gilks, and C. Berzuni. Following a Moving Target - Monte Carlo Inference for Dynamic Bayesian Models. Journal of the Royal Statistical Society B, Vol. 63, pp. 127-146, 2001.
5. A. Doucet. On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistical Computation, Vol. 10, No. 3, pp. 197-208, 2000.
6. K. Kastella, and S. Musick, The Search for Optimal Sensor Management, SPIE Symposium on Aerospace/Defense Sensing and Control (1996), April 1996, Vol. 2759, pp. 318-329.
7. C. M. Kreucher, A. O. Hero, III, and K. Kastella, A Comparison of Task Driven and Information Driven Sensor Management for Target Tracking, 44th IEEE Conference on Decision and Control (CDC) Special Session on Information Theoretic Methods for Target Tracking (2005), December 12-15.
8. K. J. Hintz, and E. S. McVey. Multi-Process Constrained Estimation. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 21, No. 1, pp. 237-244, 1991.
9. K. J. Hintz. A Measure of Information Gain Attributable to Cueing. IEEE Transactions on Systems, Science, and Cybernetics, Vol. 21, No. 2, pp. 237-244, 1991.
10. J. Manyika, and H. Durrant-Whyte, Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach, Ellis Horwood, New York, 1994.
11. W. Schmaedeke, and K. Kastella, Event-Averaged Maximum Likelihood Estimation and Information-Based Sensor Management, SPIE (1994), Vol. 2232, pp. 91-96.
12. K. Kastella. Discrimination Gain to Optimize Classification. IEEE Transactions on Systems, Man & Cybernetics-Part A: Systems and Humans, Vol. 27, No. 1, pp. 112-116, 1997.
13. R. Mahler, Global Optimal Sensor Allocation, Ninth National Symposium on Sensor Fusion (1996), Vol. 1, pp. 167-172.
14. C. M. Kreucher, K. Kastella, and A. O. Hero, III, Information-Based Sensor Management for Multitarget Tracking, SPIE Signal and Data Processing of Small Targets Conference (2003), August 3 - 8, Vol. 5204, pp. 480-489.
15. C. M. Kreucher, K. Kastella, and A. O. Hero, III. Sensor Management Using an Active Sensing Approach. Signal Processing, Vol. 85, No. 3, pp. 607-624, 2005.
16. C. M. Kreucher, A. O. Hero, K. D. Kastella, and M. R. Morelande. An Information-Based Approach to Sensor Management in Large Dynamic Networks. Proceedings of the IEEE, Vol. 95, No. 5, pp. 978-999, 2007.
17. A. Rényi, On Measures of Entropy and Information, Fourth Berkeley Symposium on Mathematical Statistics and Probability (1961), Berkeley, CA, Vol. 1, pp. 547-561.
18. F. Zhao, J. Shin, and J. Reich. Information-Driven Dynamic Sensor Collaboration. Signal Processing Magazine, IEEE, Vol. 19, No. 2, pp. 61-72, 2002.
19. C. E. Shannon. A Mathematical Theory of Communication. Bell System Technical Journal, Vol. 27, pp. 379-423, 623-656, 1948.
20. B. K. Skagerstam. On the Notions of Entropy and Information. Journal of Statistical Physics, Vol. 12, No. 6, pp. 449-462, 1975.
21. E. Ertin, J. W. Fisher, and L. C. Potter, Maximum Mutual Information Principle for Dynamic Sensor Query Problems, 2nd International Workshop on Information Processing in Sensor Networks (2003), Palo Alto, CA, April 2003.
22. J. Liu, J. Reich, and F. Zhao. Collaborative in-Network Processing for Target Tracking. Eurasip Journal on Applied Signal Processing, Vol. 2003, No. 4, pp. 378-391, 2003.
23. A. O. Hero III, M. Ba, O. Michel, and J. D. Gorman. Applications of Entropic Spanning Graphs. IEEE Signal Processing Magazine, Vol. 19, No. 5, pp. 85-95, 2002.