Keywords
data association
fusion; estimation
entropy
Teoría de la información
datos de asociación
fusión
estimación
entropía
How to Cite
Abstract
As opposed to standard methods of association which rely on measures of central dispersion, entropic measures quantify multivalued relations. This distinction is especially important when high fidelity models of the sensed phenomena do not exist. The properties of entropic measures are shown to fit within the Bayesian framework of hierarchical sensor fusion. A method of estimating probabilistic structure for categorical and continuous valued measurements that is unbiased for finite data collections is presented. Additionally, a branch and bound method for optimal sensor suite selection suitable for either target refinement or anomaly detection is described. Finally, the methodology is applied against a known data set used in a standard data mining competition that features both sparse categorical and ontinuous valued descriptors of a target. Excellent quantitative and computational results against this data set support the conclusion that the proposed methodology is promising for general purpose low level data fusion.
References
Bar-Shalom, Y.; Li, X.; Eason, R.; Kirubarajan, T. (2001) Estimation with Applications to Tracking and Navigation. Wiley, New York.
Blackman, S.; Popoli, R. (1999) Design and Analysis of Modern Tracking Systems. Artech House, Boston.
Bossé, É.; Roy, J.; Wark, S. (2007) Concepts, Models, and Tools for Information Fusion. Artech House, Boston.
Klein, L.A. (2004) Sensor and Data Fusion: A Tool for Information Assessment and Decision Making. SPIE Press, Bellingham, WA.
Hall, D.L.; McMullen, S.A.H. (2004) Mathematical Techniques in Multisensor Fusion, 2 ed. Artech House, Boston.
Antony, R.T. (1995) Principles of Data Fusion Automation. Artech House, Boston.
Liggins, M.E.; Hall, D.L.; Llinas, J. (2009) Handbook of Multisensor Data Fusion: Theory and Practice, 2 ed. CRC Press, New York.
Hastie, T.; Tibhirani, R.; Friedman, J. (2009) The Elements of Statistical Learning, 2 ed. Springer, New York.
Hero, A.O.; Kreucher, C.M.; Blatt, D. (2008) “Information theoretic approaches to sensor management”, in: Hero, Castanon, Cochran & Kastella (Eds.) Foundations and Applications of Sensor Management, Springer, New York: 33–57.
Varshney, P.K. (1997) Distributed Detection and Data Fusion. Springer, New York.
Mahler, R.P.S. (2007) Statistical Multisource-Multi-Target Information Fusion. Artech House, Boston.
Schuck, T.M.; Hunter, B.; Wilson, D.D. (2009) “Developing information fusion methods for combat identification”, in: M.E. Liggins, D.L. Hall & J. Llinas (Eds.) Handbook of Multisensory Data Fusion: Theory and Practice, 2 ed. CRC Press, New York.
Kreucher, C.; Kastella, K.; Hero, A.O. (2005) “Sensor management using an active sensing approach”, Sig. Proc. 85(3): 607–624.
Aughenbaugh, J.M.; LaCour, B.R. (2008) “Metric selection for information theoretic sensor management”, 11th International Conference on Information Fusion.
Roman, S. (1992) Coding and Information Theory. Springer, New York.
Bell, C.B. (1962) “Mutual information and maximal correlation as measures of dependence”, Ann. Math. Stat. 33: 587–595.
Cover, T.M.; Thomas, J.A. (1991) Elements of Information Theory. Wiley, New York.
Hall, P.; Morton, S.C. (1993) “On the estimation of entropy”, Ann. Inst. of Stat. Math. 45(1): 69–88.
Scott, D.W. (1992) Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley, New York.
Defense.gov News Transcript: DoD News Briefing - Secretary Rumsfeld and Gen. Myers, United States Department of Defense (defense.gov), February 12, 2002.
Simonin, C.; LeCadre, J.; Dambreville, F. (2007) “The cross-entropy method for solving a variety of hierarchial search problems”, 10th International Conference on Information Fusion.
Fukunaga, K. (1990) Introduction to Statistical Pattern Recognition. Academic Press, London.
Yeung, R.W. (1991) “A new outlook on Shannon’s information measures”, IEEE Trans. Info. Theory 37(3): 466–474.
Deignan, P.B.; Franchek, M.A.; Meckl, P.H. (2002) “Efficient information-theoretic model input selection”, 45th Midwest Symp. Circuits and Systems 1: I-635–8.
Comments
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright (c) 2011 Paul B. Deignan