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article

Multisensor data fusion: A review of the state-of-the-art

Authors:

Bahador Khaleghi,

Fakhreddine O. Karray,

Saiedeh N. RazaviAuthors Info & Claims

Information Fusion, Volume 14, Issue 1

Pages 28 - 44

https://doi.org/10.1016/j.inffus.2011.08.001

Published: 01 January 2013 Publication History

Abstract

There has been an ever-increasing interest in multi-disciplinary research on multisensor data fusion technology, driven by its versatility and diverse areas of application. Therefore, there seems to be a real need for an analytical review of recent developments in the data fusion domain. This paper proposes a comprehensive review of the data fusion state of the art, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies. In addition, several future directions of research in the data fusion community are highlighted and described.

References

[1]

Bloch, I., Information combination operators for data fusion: a comparative review with classification. IEEE Transactions on SMC: Part A. v26 i1. 52-67.

[2]

Hall, D.L. and Llinas, J., An introduction to multisensor fusion. Proceedings of the IEEE. v85 i1. 6-23.

[3]

Approaches to multisensor data fusion in target tracking: a survey. IEEE Transactions on Knowledge and Data Engineering. v18 i12. 1696-1710.

[4]

Goshtasby, A.A. and Nikolov, S., Image fusion: advances in the state of the art. Information Fusion. v8 i2. 114-118.

[5]

Information fusion for computer security: state of the art and open issues. Information Fusion. v10 i4. 274-284.

[6]

H. Wache, T. Vögele, U. Visser, H. Stuckenschmidt, G. Schuster, H. Neumann, S. Hübner, Ontology-based integration of information - a survey of existing approaches, in: Proc. of the IJCAI - Workshop on Ontologies and Information Sharing, 2001, pp. 108-117.

[7]

G.L. Rogova, V. Nimier, Reliability in information fusion: literature survey, in: Proc. of the International Conference on Information Fusion, 2004, pp. 1158-1165.

[8]

Yao, J.T., Raghavan, V.V. and Wu, Z., Web information fusion: a review of the state of the art. Information Fusion. v9 i4. 446-449.

[9]

F.E. White, Data Fusion Lexicon, Joint Directors of Laboratories, Technical Panel for C3, Data Fusion Sub-Panel, Naval Ocean Systems Center, San Diego, 1991.

[10]

Klein, L.A., Sensor and Data Fusion Concepts and Applications. 1999. second ed. Society of Photo-optical Instrumentation Engineers (SPIE), Bellingham, WA.

[11]

H. Boström, S.F. Andler, M. Brohede, R. Johansson, A. Karlsson, J. van Laere, L. Niklasson, M. Nilsson, A. Persson, T. Ziemke, On the Definition of Information Fusion as a Field of Research, Informatics Research Centre, University of Skövde, Tech. Rep. HS-IKI-TR-07-006, 2007.

[12]

E.L. Walts, Data fusion for C3I: a tutorial, in: Command, Control, Communications Intelligence (C3I) Handbook, EW Communications Inc., Palo Alto, CA, 1986, pp. 217-226.

[13]

A.N. Steinberg, C.L. Bowman, F.E. White, Revisions to the JDL data fusion model, in: Proc. of the SPIE Conference on Sensor Fusion: Architectures, Algorithms, and Applications III, 1999, pp. 430-441.

[14]

J. Llinas, C. Bowman, G. Rogova, A. Steinberg, E. Waltz, F.E. White, Revisiting the JDL data fusion model II, in: Proc. of the International Conference on Information Fusion, 2004, pp. 1218-1230.

[15]

Dasarathy, B.V., Decision Fusion. 1994. IEEE Computer Society Press, Los Alamitos, CA.

[16]

Goodman, I.R., Mahler, R.P.S. and Nguyen, H.T., Mathematics of Data Fusion. 1997. Kluwer Academic Publishers, Norwell, MA.

[17]

Kokar, M.M., Tomasik, J.A. and Weyman, J., Formalizing classes of information fusion systems. Information Fusion. v5 i3. 189-202.

[18]

M. Kumar, D.P. Garg, R.A. Zachery, A generalized approach for inconsistency detection in data fusion from multiple sensors, in: Proc. of the American Control Conference, 2006, pp. 2078-2083.

[19]

Smets, P., Analyzing the combination of conflicting belief functions. Information Fusion. v8 i4. 387-412.

[20]

Mahler, R.P.S., statistics 101 for multisensor, multitarget data fusion. IEEE Aerospace and Electronic Systems Magazine. v19 i1. 53-64.

[21]

Joshi, R. and Sanderson, A.C., Multisensor Fusion: A Minimal Representation Framework. 1999. World Scientific.

[22]

Dong, X.L., Berti-Equille, L. and Srivastava, D., Truth discovery and copying detection in a dynamic world. Journal Proceedings of the VLDB Endowment. v2 i1. 562-573.

[23]

Zhu, Y., Song, E., Zhou, J. and You, Z., Optimal dimensionality reduction of sensor data in multisensor estimation fusion. IEEE Transactions on Signal Processing. v53 i5. 1631-1639.

[24]

B.L. Milenova, M.M. Campos, Mining high-dimensional data for information fusion: a database-centric approach, in: Proc. of International Conference on Information Fusion, 2005, pp. 638-645.

[25]

Krause, P. and Clark, D., Representing Uncertain Knowledge: An Artificial Intelligence Approach. 1993. Kluwer Academic Publishers, Norwell, MA.

[26]

Smets, P., Imperfect information: imprecision and uncertainty. In: Motro, A., Smets, P. (Eds.), Uncertainty Management in Information Systems: From Needs to Solutions, Kluwer Academic Publishers., Norwell, MA. pp. 225-254.

[27]

Klir, G.J. and Wierman, M.J., Uncertainty-Based Information: Elements of Generalized Information Theory. 1999. second ed. Physica-Verlag HD, New York.

[28]

Dubois, D. and Prade, H., Formal representations of uncertainty. In: Bouyssou, D., Dubois, D., Pirlot, M., Prade, H. (Eds.), Decision-Making Process-Concepts and Methods, ISTE & Wiley, London. pp. 85-156.

[29]

M.C. Florea, A.L. Jousselme, E. Bosse, Fusion of Imperfect Information in the Unified Framework of Random Sets Theory: Application to Target Identification, Defence R&D Canada, Valcartier, Tech. Rep. ADA475342, 2007.

[30]

Komorowski, J., Pawlak, Z., Polkowski, L. and Skowron, A., Rough sets: a tutorial. In: Pal, S.K., Skowron, A. (Eds.), Rough Fuzzy Hybridization: A New Trend in Decision Making, Springer, Singapore. pp. 1-98.

[31]

Sheridan, F.K.J., A survey of techniques for inference under uncertainty. Artificial Intelligence Review. v5 i1-2. 89-119.

[32]

Durrant-Whyte, H.F. and Henderson, T.C., Multisensor data fusion. In: Siciliano, B., Khatib, O. (Eds.), Handbook of Robotics, Springer. pp. 585-610.

[33]

Zadeh, L.A., Fuzzy sets. Information and Control. v8 i3. 338-353.

[34]

Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems. v1 i1. 3-28.

[35]

Pawlak, Z., Rough Sets: Theoretical Aspects of Reasoning about Data. 1992. Kluwer Academic Publishers, Norwell, MA.

[36]

Shafer, G., A Mathematical Theory of Evidence. 1976. Princeton University Press.

[37]

Dubois, D. and Prade, H., Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems. v17 i2-3. 191-209.

[38]

Yen, J., Generalizing the Dempster-Shafer theory to fuzzy sets. IEEE Transactions on SMC. v20 i3. 559-570.

[39]

Mahler, R.P.S., Statistical Multisource-Multitarget Information Fusion. 2007. Artech House, Boston, MA.

[40]

G. Welch, G. Bishop, An Introduction to the Kalman Filter, Department of Computer Science, University of North Carolina, Tech. Rep. TR-95-041, 1995.

[41]

S.J. Julier, J.K. Uhlmann, A new extension of the Kalman filter to nonlinear systems, in: International Symposium on Aerospace/Defense Sensing, Simulation and Controls, 1997, pp. 182-193.

[42]

Stone, L.D., Corwin, T.L. and Barlow, C.A., Bayesian Multiple Target Tracking. 1999. Artech House Inc., Norwood, MA.

[43]

Doucet, A., de Freitas, N. and Gordon, N., Sequential Monte Carlo Methods in Practice (Statistics for Engineering and Information Science). 2001. Springer, New York.

[44]

Berg, B.A., Markov Chain Monte Carlo Simulations and Their Statistical Analysis. 2004. World Scientific, Singapore.

[45]

Crisan, D. and Doucet, A., A survey of convergence results on particle filtering methods for practitioners. IEEE Transactions on Signal Processing. v50 i3. 736-746.

[46]

Gordon, N.J., Salmond, D.J. and Smith, A.F.M., Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings - F. v140 i2. 107-113.

[47]

Pitt, M.K. and Shephard, N., Filtering via simulation: auxiliary particle filters. Journal of the American Statistical Association. v94 i446. 590-599.

[48]

Metropolis, N. and Ulam, S., The Monte Carlo method. Journal of American Statistics Association. v44 i247. 335-341.

[49]

Hastings, W.K., Monte Carlo sampling methods using markov chains and their applications. Biometrika. v57 i1. 97-109.

[50]

Casella, G. and George, E.I., Explaining the Gibbs sampler. The American Statistician. v46 i3. 167-174.

[51]

Rosenthal, J.S., Parallel computing and Monte Carlo algorithms. Far East Journal of Theoretical Statistics. v4. 207-236.

[52]

Cowles, M.K. and Carlin, B.P., Markov Chain Monte Carlo convergence diagnostics: a comparative review. Journal of American Statistics Accosication. v91 i434. 883-904.

[53]

Dempster, A.P., A generalization of Bayesian inference (with discussion). Journal of the Royal Statistical Society Series B. v30 i2. 205-247.

[54]

T.D. Garvey, J.D. Lowrance, M.A. Fischler, An inference technique for integrating knowledge from disparate sources, in: Proc. of the International Joint Conference on Artificial Intelligence, 1981, pp. 319-325.

[55]

B.R. Bracio, W. Horn, D.P.F. Moller, Sensor fusion in biomedical systems, in: Proc. of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1997, pp. 1387-1390.

[56]

J.A. Barnett, Computational methods for a mathematical theory of evidence, in: Proc. of the International Joint Conference on Artificial Intelligence, 1981, pp. 868-875.

[57]

Gordon, J. and Shortliffe, E.H., A method for managing evidential reasoning in a hierarchical hypothesis space. Artificial Intelligence. v26 i3. 323-357.

[58]

A. Benavoli, B. Ristic, A. Farina, M. Oxenham, L. Chisci, An approach to threat assessment based on evidential networks, in: Proc. of the International Conference on Information Fusion, 2007, pp. 1-8.

[59]

H.H.S. Ip, H. Tang, Parallel evidence combination on a SB-tree architecture, in: Proc. of the Australian and New Zealand Conference on Intelligent Information Systems, 1996, pp. 31-34.

[60]

Bauer, M., Approximation algorithms and decision making in the Dempster-Shafer theory of evidence - an empirical study. International Journal of Approximate Reasoning. v17 i2-3. 217-237.

[61]

Denoeux, T. and Yaghlane, A.B., Approximating the combination of belief functions using the fast Möbius transform in a coarsened frame. International Journal of Approximate Reasoning. v31 i1-2. 77-101.

[62]

M. Oxenham, The effect of finite set representations on the evaluation of Dempster's rule of combination, in: Proc. of the International Conference on Information Fusion, 2008, pp. 1-8.

[63]

Shenoy, P.P. and Shafer, G., Axioms for probability and belief-function propagation. In: Yager, R.R., Liu, L. (Eds.), Classic Works of the Dempster-Shafer Theory of Belief Functions, Springer, Berlin/Heidel-Berg. pp. 499-528.

[64]

Dubois, D. and Prade, H., Possibility theory and data fusion in poorly informed environments. Control Engineering Practice. v2 i5. 811-823.

[65]

P.J. Escamilla-Ambrosio, N. Mort, Hybrid Kalman filter-fuzzy logic adaptive multisensor data fusion architectures, in: Proc. of the IEEE Conference on Decision and Control, 2003, pp. 5215-5220.

[66]

J.Z. Sasiadek, P. Hartana, Sensor data fusion using Kalman filter, in: Proc. of the International Conference on Information Fusion, 2000, pp. WED5/19-WED5/25.

[67]

Zhu, H. and Basir, O., A novel fuzzy evidential reasoning paradigm for data fusion with applications in image processing. Soft Computing Journal - A Fusion of Foundations, Methodologies and Applications. v10 i12. 1169-1180.

[68]

Dubois, D. and Prade, H., Possibility Theory: An Approach to Computerized Processing of Uncertainty. 1988. Plenum Press.

[69]

D. Dubois, H. Prade, Possibility theory in information fusion, in: Proc. of the International Conference on Information Fusion, 2000, pp. PS6-PS19.

[70]

Destercke, S., Dubois, D. and Chojnacki, E., Possibilistic information fusion using maximal coherent subsets. IEEE Transactions on Fuzzy Systems. v17 i1. 79-92.

[71]

Dubois, D. and Prade, H., When upper probabilities are possibility measures. Fuzzy Sets and Systems. v49 i1. 65-74.

[72]

Borotschnig, H., Paletta, L., Prantl, M. and Pinz, A., Comparison of probabilistic, possibilistic and evidence theoretic fusion schemes for active object recognition. Computing. v62 i4. 293-319.

[73]

Reasoning with multiple-source information in a possibilistic logic framework. Information Fusion. v7 i1. 80-96.

[74]

Pawlak, Z. and Skowron, A., Rudiments of rough sets. Information Sciences. v177 i1. 3-27.

[75]

L. Yong, X. Congfu, P. Yunhe, A new approach for data fusion: implement rough set theory in dynamic objects distinguishing and tracing, in: Proc. of the IEEE International Conference on SMC, 2004, pp. 3318-3322.

[76]

J.F. Peters, S. Ramanna, A. Skowron, J. Stepaniuk, Z. Suraj, Sensor fusion: a rough granular approach, in: Proc. of the IFSA World Congress and 20th NAFIPS International Conference, 2001, pp. 1367-1371.

[77]

W. Haijun, C. Yimin, Sensor data fusion using rough set for mobile robots system, in: Proc. of the IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, 2006, pp. 1-5.

[78]

Yager, R.R., Generalized probabilities of fuzzy events from fuzzy belief structures. Information Sciences. v28 i1. 45-62.

[79]

Basir, O., Karray, F. and Hongwei, Z., Connectionist-based Dempster-Shafer evidential reasoning for data fusion. IEEE Transactions on Neural Networks. v6 i6. 1513-1530.

[80]

Yeung, D.S., Degang, C., Tsang, E.C.C., Lee, J.W.T. and Wang, X., On the generalization of fuzzy rough sets. IEEE Transactions on Fuzzy Systems. v13 i3. 343-361.

[81]

Guan, T. and Feng, B., Rough fuzzy integrals for information fusion and classification. Lecture Notes in Computer Science. v3066. 362-367.

[82]

Kendall, D.G., Foundations of a theory of random sets. In: Hardings, E.F., Kendall, D.G. (Eds.), Stochastic Geometry, J. Wiley. pp. 322-376.

[83]

Kreinovich, V., Random sets unify, explain, and aid known uncertainty methods in expert systems. In: Goutsias, J., Mahler, R.P.S., Nguyen, H.T. (Eds.), Random Sets: Theory and Applications, Springer-Verlag. pp. 321-345.

[84]

Mori, S., Random sets in data fusion. Multi-object state-estimation as a foundation of data fusion theory. In: Goutsias, J., Mahler, R.P.S., Nguyen, H.T. (Eds.), Random Sets: Theory and Applications, Springer-Verlag. pp. 185-207.

[85]

R.P.S. Mahler, Random sets: unification and computation for information fusion - a retrospective assessment, in: Proc. of the International Conference on Information Fusion, 2004, pp. 1-20.

[86]

Mahler, R.P.S., PHD filters of higher order in target number. IEEE Transactions on Aerospace and Electronic Systems. v43 i4. 1523-1543.

[87]

B.T. Vo, B.N. Vo, A. Cantoni, The cardinalized probability hypothesis density filter for linear Gaussian multi-target models, in: Proc. of the Annual Conference on Information Sciences and Systems, 2006, pp. 681-686.

[88]

Vo, B.T., Vo, B.N. and Cantoni, A., Analytic implemenations of the cardinalized probability hypothesis density filter. IEEE Transactions on Signal Processing. v55 i7. 3553-3567.

[89]

Vo, B.N. and Ma, W.K., The Gaussian mixture probability hypothesis density filter. IEEE Transactions on Signal Processing. v54 i11. 4091-4104.

[90]

Vo, B.N., Singh, S. and Doucet, A., Sequential Monte Carlo methods for multi-target filtering with random finite sets. IEEE Transactions on Aerospace and Electronic Systems. v41 i4. 1224-1245.

[91]

K. Panta, B.N. Vo, S. Singh, A. Doucet, Probability hypothesis density versus multiple hypothesis tracking, in: Proc. of the SPIE Conference on Signal Processing, Sensor Fusion and Target Recognition, 2004, pp. 284-295.

[92]

R.P.S. Mahler, A survey of PHD filter and CPHD filter implementations, in: Proc. of the SPIE Conference on Signal Processing, Sensor Fusion, and Target Recognition, 2007, pp. 65670O1-65670O12.

[93]

D. Angelosante, E. Biglieri, M. Lops, Multiuser detection in a dynamic environment: joint user identification and parameter estimation, in: Proc. of the IEEE International Symposium on Information Theory, 2007, pp. 2071-2075.

[94]

M. Maehlisch, R. Schweiger, W. Ritter, K. Dietmayer, Multisensor vehicle tracking with the probability hypothesis density filter, in: Proc. of the International Conference on Information Fusion, 2006, pp. 1-8.

[95]

R.P.S. Mahler, Sensor management with non-ideal sensor dynamics, in: Proc. of the International Conference on Information fusion, 2004, pp. 783-790.

[96]

B. Khaleghi, A. Khamis, F. Karray, Random finite set theoretic based soft/hard data fusion with application for target tracking, in: Proc. of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2010, pp. 50-55.

[97]

X. Mingge, H. You, H. Xiaodong, S. Feng, Image fusion algorithm using rough sets theory and wavelet analysis, in: Proc. of the International Conference on Signal Processing, 2004, pp. 1041-1044.

[98]

S.J. Julier, J.K. Uhlmann, A non-divergent algorithm in the presence of unknown correlation, in: Proc. of the American Control Conference, 1997, pp. 2369-2373.

[99]

A. Makarenko, A. Brooks, T. Kaupp, H.F. Durrant-Whyte, F. Dellaert, Decentralised data fusion: a graphical model approach, in: Proc. of the International Conference on Information Fusion, 2009, pp. 545-554.

[100]

Maybeck, P.S., . 1979. Estimation and Control, 1979.Academic Press, New York.

[101]

J.K. Uhlmann, S.J. Julier, H.F. Durrant-Whyte, A Culminating Advance in the Theory and Practice of Data Fusion, Filtering and Decentralized Estimation, Covariance Intersection Working Group, Tech. Rep., 1997.

[102]

Chong, C.Y., Mori, S. and Chang, K.C., Distributed multitarget multisensor tracking. In: Bar-Shalom, Y. (Ed.), Multitarget-Multisensor Tracking: Advanced Applications, Artech House, Norwood, MA. pp. 247-295.

[103]

K.C. Chang, C.Y. Chong, S. Mori, On scalable distributed sensor fusion, in: Proc. of the International Conference on Information Fusion, 2008, pp. 1-8.

[104]

A.D. Marrs, C.M. Reed, A.R. Webb, H.C. Webber, Data Incest and Symbolic Information Processing, United Kingdom Defence Evaluation and Research Agency, Tech. Rep., 1999.

[105]

S.P. McLaughlin, R.J. Evans, V. Krishnamurthy, Data incest removal in a survivable estimation fusion architecture, in: Proc. of the International Conference on Information Fusion, 2003, pp. 229-236.

[106]

L.Y. Pao, M. Kalandros, Algorithms for a class of distributed architecture tracking, in: Proc. of the American Control Conference, 1997, pp. 1434-1438.

[107]

S.P. McLaughlin, R.J. Evans, V. Krishnamurthy, A graph theoretic approach to data incest management in network centric warfare, in: Proc. of the International Conference on Information Fusion, 2005, pp. 1162-1169.

[108]

T. Brehard, V. Krishnamurthy, Optimal data incest removal in Bayesian decentralized estimation over a sensor network, in: IEEE International Conference on Acoustics, Speech and Signal Processing, 2007, pp. III173-III176.

[109]

W. Khawsuk, L.Y. Pao, Decorrelated state estimation for distributed tracking of interacting targets in cluttered environments, in: Proc. of the American Control Conference, 2002, pp. 899-904.

[110]

M.B. Hurley, An information theoretic justification for covariance intersection and its generalization, in: Proc. of the International Conference on Information Fusion, 2002, pp. 505-511.

[111]

Chen, L., Arambel, P.O. and Mehra, R.K., Estimation under unknown correlation: covariance intersection revisited. IEEE Transactions on Automatic Control. v47 i11. 1879-1882.

[112]

W. Niehsen, Information fusion based on fast covariance intersection filtering, in: Proc. of the International Conference on Information Fusion, 2002, pp. 901-904.

[113]

D. Franken, A. Hupper, Improved fast covariance intersection for distributed data fusion, in: Proc. of the International Conference on Information Fusion, 2005, pp. 154-160.

[114]

A.R. Benaskeur, Consistent fusion of correlated data sources, in: Proc. of IEEE Annual Conference of the Industrial Electronics Society, 2002, pp. 2652-2656.

[115]

Y. Zhou, J. Li, Robust decentralized data fusion based on internal ellipsoid approximation, in: Proc. of World Congress of the International Federation of Automatic Control, 2008, pp. 9964-9969.

[116]

W.J. Farrell, C. Ganesh, Generalized chernoff fusion approximation for practical distributed data fusion, in: Proc. of the International Conference on Information Fusion, 2009, pp. 555-562.

[117]

Djurovic, Z. and Kovacevic, B., QQ-plot approach to robust Kalman filtering. International Journal of Control. v61 i4. 837-857.

[118]

S.J. Wellington, J.K. Atkinson, R.P. Sion, Sensor validation and fusion using the Nadaraya-Watson statistical estimator, in: Proc. of the International Conference on Information Fusion, 2002, pp. 321-326.

[119]

Ibarguengoytia, P.H., Sucar, L.E. and Vadera, V., Real time intelligent sensor validation. IEEE Transactions on Power Systems. v16 i4. 770-775.

[120]

Frolik, J., Abdelrahman, M. and Kandasamy, P., A confidence-based approach to the self-validation, fusion and reconstruction of quasiredundant sensor data. IEEE Transactions on Instrumentation and Measurement. v50 i6. 1761-1769.

[121]

Kumar, M., Garg, D.P. and Zachery, R.A., A method for judicious fusion of inconsistent multiple sensor data. IEEE Sensors Journal. v7 i5. 723-733.

[122]

M. Kumar, D. Garg, R. Zachery, Stochastic adaptive sensor modeling and data fusion, in: Proc. of the SPIE Conference on Smart Structures and Materials: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 2006, pp. 100-110.

[123]

U. Orguner, F. Gustafsson, Storage efficient particle filters for the out of sequence measurement problem, in: Proc. of the International Conference on Information Fusion, 2008, pp. 1-8.

[124]

Blackman, S.S. and Popoli, R., Design and Analysis of Modern Tracking Systems. 1999. Artech House, Berlin.

[125]

Bar-Shalom, Y., Update with out-of-sequence measurements in tracking: exact solution. IEEE Transactions on Aerospace and Electronic Systems. v38 i3. 769-777.

[126]

M. Mallick, S. Coraluppi, C. Carthel, Advances in asynchronous and decentralized estimation, in: Proc. of the IEEE Aerospace Conference, 2001, pp. 4/1873-4/1888.

[127]

K. Zhang, X.R. Li, Y. Zhu, Optimal update with out-of-sequence measurements for distributed filtering, in: Proc. of the International Conference on Information Fusion, 2002, pp. 1519-1526.

[128]

Zhang, K., Li, X.R. and Zhu, Y., Optimal update with out-of-sequence measurements. IEEE Transactions on Signal Processing. v53 i6. 1992-2004.

[129]

Bar-Shalom, Y., Chen, H. and Mallick, M., One-step solution for the multistep out-of-sequence measurement problem in tracking. IEEE Transactions on Aerospace and Electronic Systems. v40 i1. 27-37.

[130]

Y. Anxi, L. Diannong, H. Weidong, D. Zhen, A unified out-of-sequence measurements filter, in: Proc. of the IEEE International Radar Conference, 2005, pp. 453-458.

[131]

M. Mallick, J. Krant, Y. Bar-Shalom, Multi-sensor multi-target tracking using out-of-sequence measurements, in: Proc. of the International Conference on Information Fusion, 2002, pp. 135-142.

[132]

Maskell, S.R., Everitt, R.G., Wright, R. and Briers, M., Multi-target out-of-sequence data association: tracking using graphical models. Information Fusion. v7 i4. 434-447.

[133]

S. Challa, J.A. Legg, Track-to-track fusion of out-of-sequence tracks, in: Proc. of the International Conference on Information Fusion, 2002, pp. 919-926.

[134]

A. Novoselsky, S.E. Sklarz, M. Dorfan, Track to track fusion using out-of-sequence track information, in: Proc. of the International Conference on Information Fusion, 2007, pp. 1-5.

[135]

Review of "a mathematics theory of evidence". AI Magazine. v5 i3. 81-83.

[136]

Florea, M.C., Jousselme, A.L., Bosse, E. and Grenier, D., Robust combination rules for evidence theory. Information Fusion. v10 i2. 183-197.

[137]

Yager, R.R., On the Dempster-Shafer framework and new combination rules. Information Sciences. v41 i2. 93-137.

[138]

Smets, P., The combination of evidence in the transferable belief model. IEEE Transactions on Pattern Analysis and Machince Intelligence. v12 i5. 447-458.

[139]

Dezert, J., Foundations for a new theory of plausible and paradoxical reasoning. Information Security. v9. 13-57.

[140]

Lefevre, E., Colot, O. and Vannoorenberghe, P., Belief function combination and the conflict management. Information Fusion. v3 i2. 149-162.

[141]

Voorbraak, F., On the justification of Dempster's rule of combination. Artificial Intelligence. v48 i2. 171-197.

[142]

Haenni, R., Are alternatives to Dempster's rule of combination real alternatives? Comments on "About the belief combination and the conflict management problem" - Lefevre et al. Information Fusion. v3 i3. 237-239.

[143]

Dezert, J., Martin, A. and Smarandache, F., Comments on "A new combination of evidence based on compromise" by K. Yamada. Fuzzy Sets and Systems. v160. 853-855.

[144]

Uhlmann, J.K., Covariance consistency methods for fault-tolerant distributed data fusion. Information Fusion. v4 i3. 201-215.

[145]

Maskell, S., A Bayesian approach to fusing uncertain, imprecise and conflicting information. Information Fusion. v9 i2. 259-277.

[146]

F. Rheaume, A.R. Benaskeur, Forward prediction-based approach to target-tracking with out-of-sequence measurements, in: Proc. of the IEEE Conference on Decision and Control, 2008, pp. 1326-1333.

[147]

D.L. Hall, M. McNeese, J. Llinas, T. Mullen, A framework for dynamic hard/soft fusion, in: Proc. of the International Conference on Information Fusion, 2008, pp. 1-8.

[148]

M.A. Pravia, R.K. Prasanth, P.O. Arambel, C. Sidner, C.Y. Chong, Generation of a fundamental data set for hard/soft information fusion, in: Proc. of the International Conference on Information Fusion, 2008, pp. 1-8.

[149]

M.A. Pravia, O. Babko-Malaya, M.K. Schneider, J.V. White, C.Y. Chong, Lessons learned in the creation of a data set for hard/soft information fusion, in: Proc. of the International Conference on Information Fusion, 2009, pp. 2114-2121.

[150]

K. Premaratne, M.N. Murthi, J. Zhang, M. Scheutz, P.H. Bauer, A Dempster-Shafer theoretic conditional approach to evidence updating for fusion of hard and soft data, in: Proc. of the International Conference on Information Fusion, 2009, pp. 2122-2129.

[151]

A. Auger, J. Roy, Expression of uncertainty in linguistic data, in: Proc. of the International Conference on Information Fusion, 2008, pp. 1-8.

[152]

D.L. Hall, M.D. McNeese, D.B. Hellar, B.J. Panulla, W. Shumaker, A cyber infrastructure for evaluating the performance of human centered fusion, in: Proc. of the International Conference on Information Fusion, 2009, pp. 1257-1264.

[153]

K. Sambhoos, J. Llinas, E. Little, Graphical methods for real-time fusion and estimation with soft message data, in: Proc. of the International Conference on Information Fusion, 2008, pp. 1-8.

[154]

G. Ferrin, L. Snidaro, S. Canazza, G.L. Foresti, Soft data issues in fusion of video surveillance, in: Proc. of the International Conference on Information Fusion, 2008, pp. 1-8.

[155]

S. Challa, T. Gulrez, Z. Chaczko, T.N. Paranesha, Opportunistic information fusion: a new paradigm for next generation networked sensing systems, in: Proc. of the International Conference on Information Fusion, 2005, pp. 720-727.

[156]

C. Wu, H. Aghajan, Model-based human posture estimation for gesture analysis in an opportunistic fusion smart camera network, in: Proc. of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2007, pp. 453-458.

[157]

R. Al-Hmouz, S. Challa, Optimal placement for opportunistic cameras using genetic algorithm, in: Proc. of the International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2005, pp. 337-341.

[158]

L. Hong, Adaptive data fusion, in: Proc. of the IEEE International Conference on SMC, 1991, pp. 767-772.

[159]

G. Loy, L. Fletcher, N. Apostoloff, A. Zelinsky, An adaptive fusion architecture for target tracking, in: Proc. of the IEEE International Conference on Automatic Face and Gesture Recognition, 2002, pp. 261-266.

[160]

P.J. Escamilla-Ambrosio, N. Mort, Hybrid Kalman filter-fuzzy logic adaptive multisensor data fusion architectures, in: Proc. of the IEEE Conference on Decision and Control, 2003, pp. 5215-5220.

[161]

A.D. Tafti, N. Sadati, Novel adaptive Kalman filtering and fuzzy track fusion approach for real time applications, in: Proc. of the IEEE Conference on Industrial Electronics and Applications, 2008, pp. 120-125.

[162]

H.E. Soken, C. Hajiyev, Adaptive unscented Kalman filter with multiple fading factors for pico satellite attitude estimation, in: Proc. of the International Conference on Recent Advances in Space Technologies, 2009, pp. 541-546.

[163]

Huang, X. and Oviatt, S., Toward adaptive information fusion in multimodal systems. Lecture Notes in Computer Science. v3869. 15-27.

[164]

Ansari, N., Hou, E.S.H., Zhu, B.O. and Chen, J.G., Adaptive fusion by reinforcement learning for distributed detection systems. IEEE Transactions on Aerospace and Electronic Systems. v32 i2. 524-531.

[165]

Hossain, M.A., Atrey, P.K. and El-Saddik, A., Learning multisensor confidence using a reward-and-punishment mechanism. IEEE Transactions on Instrumentation and Measurement. v58 i5. 1525-1534.

[166]

G. Fabeck, R. Mathar, Kernel-based learning of decision fusion in wireless sensor networks, in: Proc. of the International Conference on Information Fusion, 2008, pp. 1-7.

[167]

M.M. Kokar, K. Baclawski, H. Gao, Category theory-based synthesis of a higher-level fusion algorithm - an example, in: Proc. of the International Conference on Information fusion, 2006, pp. 1-8.

[168]

V. Nimier, Introducing contextual information in multisensor tracking algorithms, in: Proc. of the International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems, Advances in Intelligent Computing, 1994, pp. 595-604.

[169]

B. Yu, K. Sycara, Learning the quality of sensor data in distributed decision fusion, in: Proc. of the International Conference on Information Fusion, 2006, pp. 1-8.

[170]

F. Delmotte, L. Dubois, P. Borne, Context-dependent trust in data fusion within the possibility theory, in: Proc. of the IEEE International Conference on SMC, 1996, pp. 538-543.

[171]

Sandri, S.A., Dubois, D. and Kalfsbeek, H.W., Elicitation, assessment and pooling of expert judgments using possibility theory. IEEE Transactions on Fuzzy Systems. v3 i3. 313-335.

[172]

Haenni, R. and Hartmann, S., Modeling partially reliable information sources: a general approach based on Dempster-Shafer theory. Information Fusion. v7 i4. 361-379.

[173]

Elouedi, Z., Mellouli, K. and Smets, P., Assessing sensor reliability for multisensor data fusion within the transferable belief model. IEEE Transactions on SMC - Part B. v34 i1. 782-787.

[174]

E.J. Wright, K.B. Laskey, Credibility models for multi-source fusion, in: Proc. of the International Conference on Information Fusion, 2006, pp. 1-7.

[175]

A. Karlsson, Dependable and Generic High-level Information Fusion - Methods and Algorithms for Uncertainty Management, School of Humanities and Informatics, University of Skövde, Sweden, Tech. Rep. HS-IKI-TR-07-003, 2007.

[176]

M. Kefayati, M.S. Talebi, H.R. Rabiee, B.H. Khalaj, On secure consensus information fusion over sensor networks, in: Proc. of the IEEE International Conference on Computer Systems and Applications, 2007, pp. 108-115.

[177]

M. Kefayati, M.S. Talebi, B.H. Khalaj, H.R. Rabiee, Secure consensus averaging in sensor networks using random offsets, in: Proc. of IEEE International Conference on Telecommunications, 2007, pp. 556-560.

[178]

Y. Sang, H. Shen, Y. Inoguchi, Y. Tan, N. Xiong, Secure data aggregation in wireless sensor networks: a survey, in: Proc. of the IEEE International Conference on Parallel and Distributed Computing, Applications and Technologies, 2006, pp. 315-320.

[179]

H. Alzaid, E. Foo, J.G. Nieto, Secure data aggregation in wireless sensor network: a survey, in: Proc. of the Australasian Information Security Conference, 2008, pp. 93-106.

[180]

H. Chen, G. Chen, E.P. Blasch, P. Douville, K. Pham, Information theoretic measures for performance evaluation and comparison, in: Proc. of the International Conference on Information Fusion, 2009, pp. 874-881.

[181]

L. Cholvy, Information evaluation in fusion: a case study, in: Proc. of the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2004, pp. 993-1000.

[182]

STANAG 2022: Intelligence Reports, North Atlantic Treaty Organization (NATO), December 1992.

[183]

V. Nimier, Information evaluation: a formalization of operational recommendations, in: Proc. of the International Conference on Information Fusion, 2004, pp. 1166-1171.

[184]

L. Cholvy, Modelling information evaluation in fusion, in: Proc. of the International Conference on Information Fusion, 2007, pp. 1-6.

[185]

A.E. Gelfand, C. Smith, M. Colony, C. Bowman, Performance evaluation of decentralized estimation systems with uncertain communications, in: Proc. of the International Conference on Information Fusion, 2009, pp. 786-793.

[186]

O.E. Drummond, Methodologies for performance evaluation of multi-target multisensor tracking, in: Proc. of the SPIE Conference on Signal and Data Processing of Small Targets, 1999, pp. 355-369.

[187]

R.L. Rothrock, O.E. Drummond, Performance metrics for multiple-sensor multiple-target tracking, in: Proc. of the SPIE Conference on Signal and Data Processing of Small Targets, 2000, pp. 521-531.

[188]

T. Zajic, J.L. Hoffman, R.P.S. Mahler, Scientific performance metrics for data fusion: new results, in: Proc. of the SPIE Conferenec on Signal Processing, Sensor Fusion, and Target Recognition, 2000, pp. 172-182.

[189]

Schuhmacher, D., Vo, B.T. and Vo, B.N., A consistent metric for performance evaluation of multi-object filters. IEEE Transactions on Signal Processing. v56 i8. 3447-3457.

[190]

B.T. Vo, Random Finite Sets in Multi-object Filtering, Ph.D. Thesis, School of Electrical, Electronic and Computer Engineering, University of Western Australia, 2008.

[191]

P. Jackson, J.D. Musiak, Boeing fusion performance analysis (FPA) tool, in: Proc. of the International Conference on Information Fusion, 2009, pp. 1444-1450.

[192]

D. Akselrod, R. Tharmarasa, T. Kirubarajan, Z. Ding, T. Ponsford, Multisensor-multitarget tracking testbed, in: Proc. of the IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2009, pp. 1-6.

[193]

J. van Laere, Challenges for IF performance evaluation in practice, in: Proc. of the International Conference on Information Fusion, 2009, pp. 866-873.

[194]

X.R. Li, Z. Duan, Comprehensive evaluation of decision performance, in: Proc. of the International Conference on Information Fusion, 2008, pp. 1-8.

[195]

C.Y. Chong, Problem characterization in tracking/fusion algorithm evaluation, in: Proc. of the International Conference on Information Fusion, 2000, pp. 26-32.

[196]

D. Schumacher, B.T. Vo, B.N. Vo, On performance evaluation of multi-object filters, in: Proc. of the International Conference on Information Fusion, 2008, pp. 1-8.

[197]

C. Thomas, N. Balikrishnan, Modified evidence theory for performance enhancement of intrusion detection systems, in: Proc. of the International Conference on Information Fusion, 2008, pp. 1-8.

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cover image Information Fusion

Information Fusion Volume 14, Issue 1

January, 2013

122 pages

ISSN:1566-2535

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Published: 01 January 2013

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Liu JCheng GSong S(2025)Event-triggered distributed diffusion robust nonlinear filter for sensor networksSignal Processing10.1016/j.sigpro.2024.109662226:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.sigpro.2024.109662
Song LJing ZDong P(2024)Consensus Filter for Distributed Sensor Networks With Unknown Colored NoiseProceedings of the International Conference on Computing, Machine Learning and Data Science10.1145/3661725.3661738(1-6)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3661725.3661738
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https://dl.acm.org/doi/10.1145/3645029
Wu PImbiriba TElvira VClosas P(2024)Bayesian Data Fusion With Shared PriorsIEEE Transactions on Signal Processing10.1109/TSP.2023.334356472(275-288)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TSP.2023.3343564
Campagner ABarandas MFolgado DGamboa HCabitza F(2024)Ensemble Predictors: Possibilistic Combination of Conformal Predictors for Multivariate Time Series ClassificationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.338809746:11(7205-7216)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TPAMI.2024.3388097
Liu ZLetchmunan S(2024)Representing uncertainty and imprecision in machine learningJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.10190436:1Online publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.jksuci.2023.101904
Yan XJiang DXiang LXu YWang Y(2024)CDTFAFNInformation Fusion10.1016/j.inffus.2024.102554112:COnline publication date: 1-Dec-2024
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Sun DLi YLiu ZJia SNoman K(2024)Physics-inspired multimodal machine learning for adaptive correlation fusion based rotating machinery fault diagnosisInformation Fusion10.1016/j.inffus.2024.102394108:COnline publication date: 1-Aug-2024
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Wang XWang YYang JJia XLi LDing WWang F(2024)The survey on multi-source data fusion in cyber-physical-social systemsInformation Fusion10.1016/j.inffus.2024.102321107:COnline publication date: 1-Jul-2024
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Tu BRen QLi JCao ZChen YPlaza A(2024)NCGLF2Information Fusion10.1016/j.inffus.2023.102192104:COnline publication date: 1-Apr-2024
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