Definitions of Sensor Data Fusion in the Literature

[1] JDL (1987). Data fusion is “a process dealing with the association, correlation, and combination of data and information from single and multiple sources to achieve refined position and identity estimates, and complete and timely assessments of situations and threats, and their significance. The process is characterized by continuous refinements of its estimates and assessments, and the evaluation of the need for additional sources, or modification of the process itself, to achieve improved results.”

[2] Hugh Durrant-Whyte (1988). “The basic problem in multi-sensor systems is to integrate a sequence of observations from a number of different sensors into a single best-estimate of the state of the environment.”

[3] Llinas (1988). “Fusion can be defined as a process of integrating information from multiple sources to produce the most specific and comprehensive unified data about an entity, activity or event. This definition has some key operative words: specific, comprehensive, and entity. From an informationtheoretic point of view, fusion, to be effective as an information processing function, must (at least ideally) increase the specificity and comprehensiveness of the understanding we have about a battlefield entity or else there would be no purpose in performing the function.”

[4] Richardson and Marsh (1988). “Data fusion is the process by which data from a multitude of sensors is used to yield an optimal estimate of a specified state vector pertaining to the observed system.”

[5] McKendall and Mintz (1988). “...the problem of sensor fusion is the problem of combining multiple measurements from sensors into a single measurement of the sensed object or attribute, called the parameter.”

[6] Waltz and Llinas (1990). “This field of technology has been appropriately termed data fusion because the objective of its processes is to combine elements of raw data from different sources into a single set of meaningful information that is of greater benefit than the sum of the contributing parts. As a technology, data fusion is actually the integration and application of many traditional disciplines and new areas of engineering to achieve the fusion of data.”

[7] Luo and Kay (1992). “Multisensor fusion, ..., refers to any stage in an integration process where there is an actual combination (or fusion) of different sources of sensory information into one representational format.”

[8] Abidi and Gonzalez (1992). “Data fusion deals with the synergistic combination of information made available by various knowledge sources such as sensors, in order to provide a better understanding of a given scene.”

[9] Hall (1992). “Multisensor data fusion seeks to combine data from multiple sensors to perform inferences that may not be possible from a single sensor alone.”

[10] DSTO (1994). Data fusion is “a multilevel, multifaceted process dealing with the automatic detection, association, correlation, estimation, and combination of data and information from single and multiple sources.”

[11] Malhotra (1995). “The process of sensor fusion involves gathering sensory data, refining and interpreting it, and making new sensor allocation decisions.”

[12] Hall and Llinas (1997). “Data fusion techniques combine data from multiple sensors, and related information from associated databases, to achieve improved accuracy and more specific inferences than could be achieved by the use of single sensor alone.”

[13] Goodman, Mahler and Nguyen (1997). Data fusion is to “locate and identify many unknown objects of many different types on the basis of different kinds of evidence. This evidence is collected on an ongoing basis by many possibly allocatable sensors having varying capabilities and to analyze the results insuch a way as to supply local and over-all assessments of the significance of a scenario and to determine proper responses based on those assessments.”

[14] Paradis, Chalmers, Carling and Bergeron (1997). “Data fusion is fundamentally a process designed to manage (i.e., organize, combine and interpret) data and information, obtained from a variety of sources, that may be required at any time by operators or commanders for decision making. ... Data fusion is an adaptive information process that continuously transforms available data and information into richer information, through continuous refinement of hypotheses or inferences about real-world events, to achieve a refined (potentially optimal) kinematics and identity estimates of individual objects, and complete and timely assessments of current and potential future situations and threats (i.e., contextual reasoning), and their significance in the context of operational settings.”

[15] Starr and Desforges (1998). “Data fusion is a process that combines data and knowledge from different sources with the aim of maximising the useful information content, for improved reliability or discriminant capability, while minimising the quantity of data ultimately retained.”

[16] Wald (1998). “Data fusion is a formal framework in which are expressed means and tools for the alliance of data of the same scene originating from different sources. It aims at obtaining information of greater quality; the exact definition of greater quality will depend upon the application.”

[17] Evans (1998). “The combining of data from different complementary sources (usually geodemographic and lifestyle or market research and lifestyle) to build a picture of someone’s life”.

[18] Wald (1999). “Data fusion is a formal framework in which are expressed the means and tools for the alliance of data originating from different sources.”

[19] Steinberg, Bowman and White (1999). “Data fusion is the process of combining data to refine state estimates and predictions.”

[20] Gonsalves, Cunningham, Ton and Okon (2000). “The overall goal of data fusion is to combine data from multiple sources into information that has greater benefit than what would have been derived from each of the contributing parts.”

[21] Hannah, Ball and Starr (2000). “Fusion is defined materially as a process of blending, usually with the application of heat to melt constituents together (OED), but in data processing the more abstract form of union or blending together is meant. The ’heat’ is applied with a series of algorithms which, depending on the technique used, give a more or less abstract relationship between the constituents and the finished output.”

[22] Dasarathy (2001). “Information fusion encompasses the theory, techniques, and tools conceived and employed for exploiting the synergy in the information acquired from multiple sources (sensor, databases, information gathered by humans etc.) such that the resulting decision or action is in some sense better (qualitatively and quantitatively, in terms of accuracy, robustness and etc.) than would be possible, if these sources were used individually without such synergy exploitation.”

[23] Bloch and Hunter et al. (2001). “...fusion consists in conjoining or merging information that stems from several sources and exploiting that conjoined or merged information in various tasks such as answering questions, making decisions, numerical estimation, etc.”

[24] McGirr (2001). “The process of bringing large amounts of dissimilar information together into a more comprehensive and easily manageable form is known as data fusion.”

[25] Bell, Santos and Brown (2002). “Sophisticated information fusion capabilities are required in order to transform what the agents gather from a raw form to an integrated, consistent and complete form. Information fusion can occur at multiple levels of abstraction.”

[26] Challa, Gulrez, Chaczko and Paranesha (2005). Multi-sensor data fusion “is a core component of all networked sensing systems, which is used either to:- join/combine complementary information produced by sensor to obtain a more complete picture or - reduce/manage uncertainty by using sensor information from multiple sources.”

[27] Jalobeanu and Gutirrez (2006). “The data fusion problem can be stated as the computation of the posterior pdf [probability distribution function] of the unknown single object given all observations.”

[28] Mastrogiovanni et al (2007). “The aim of a data fusion process is to maximize the useful information content acquired by heterogeneous sources in order to infer relevant situations and events related to the observed environment.”

[29] Wikipedia (2007). “Information Integration is a field of study known by various terms: Information Fusion, Deduplication, Referential Integrity and so on. It refers to the field of study of techniques attempting to merge information from disparate sources despite differing conceptual, contextual and typographical representations. This is used in data mining and consolidation of data from semi- or unstructured resources.”

[30] Wikipedia (2007). “Sensor fusion is the combining of sensory data or data derived from sensory data from disparate sources such that the resulting information is in some sense better than would be possible when these sources were used individually. The term better in that case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as stereoscopic vision (calculation of depth information by combining two-dimensional images from two cameras at slightly different viewpoints). The data sources for a fusion process are not specified to originate from identical sensors. One can distinguish direct fusion, indirect fusion and fusion of the outputs of the former two. Direct fusion is the fusion of sensor data from a set of heterogeneous or homogeneous sensors, soft sensors, and history values of sensor data, while indirect fusion uses information sources like a priori knowledge about the environment and human input. Sensor fusion is also known as (multi-sensor) data fusion and is a subset of information fusion.”

[31] MSN Encarta (2007). “Data integration: the integration of data and knowledge collected from disparate sources by different methods into a consistent, accurate, and useful whole.”

[32] [2] F. E. White, Jr., Data Fusion Lexicon, Joint Directors of Laboratories, Technical Panel for C3, Data Fusion Sub-Panel, Naval Ocean Systems Center, San Diego, 1987.

[3] H. F. Durrant-Whyte, Integration, Coordination and control of Multi-sensor robot systems, Kluwer Academic Publishers. 1988.

[4] J. Llinas, Toward the Utilization of Certain Elements of AI Technology for Multi Sensor Data Fusion. In: C. J. Harris (ed.), Application of artificial intelligence to command and control systems, Peter Peregrinus Ltd (1988).

[5] J. M. Richardson and K. A. Marsh. Fusion of Multisensor data. The International Journal of Robotic Research, Vol. 7, No. 6, pp. 78-96, 1988.

[6] R. McKendall and M. Mintz. Robust fusion of location information. IEEE International Conference on Robotics and Automation, Philadelphia, United States, pp. 1239-1244. April 1988.

[7] E. L. Waltz and J. Llinas Multisensor Data Fusion. Artech House, Inc. Norwood, MA, USA. 1990.

[8] R. C. Luo and M. G Kay. Data fusion and sensor integration: State-of-the-art 1990s. Data Fusion in Robotics and Machine Intelligence, Academic Press Limited, San Diego, 1992

[9] M. A. Abidi and R. C. Gonzalez, Data Fusion in Robotics and Machine Intelligence, Academic Press, San Diego. 1992.

[10] David L. Hall, Mathematical Techniques in Multisensor Data Fusion, Artech House (1992).

[11] DSTO (Defence Science and Technology Organization) Data Fusion Special Interest Group, Data fusion lexicon. Department of Defence, Australia, 7 p., 21 September 1994.

[12] R. Malhotra. Temporal considerations in sensor management, In: Proceedings of the IEEE national aerospace and electronics conference, NAECON 1995.

[13] D.L. Hall and J. Llinas - An Introduction to Multisensor Fusion. In: Proceedings of the IEEE, vol. 85. issue 1. p 6-23. Jan 1997.

[14] I. R. Goodman, R. P. Mahler and H. T. Nguyen, Mathematics of Data Fusion, Kluwer Academic Publishers, 1997.

[15] S. Paradis, B. A. Chalmers, R. Carling, P. Bergeron, Towards a generic model for situation and threat assessment, SPIE vol. 3080, 1997.

[16] Starr and M. Desforges. Strategies in data fusion - sorting through the tool box. Proceedings of European Conference on Data Fusion, 1998.

[17] L. Wald. A European proposal for terms of reference in data fusion, In: International Archives of Photogrammetry and Remote Sensing, Vol. XXXII, Part 7, pp. 651-654. 1998.

[18] M. Evans. From 1086 to 1984: direct marketing into the millennium, Marketing Intelligence and Planning, 16(1), pp.56-67, 1998.

[19] L. Wald. Some terms of reference in data fusion. In: IEEE Transactions on Geosciences and Remote Sensing, 37, 3, pp. 1190-1193. 1999.

[20] A. N. Steinberg, C. L. Bowman and F. E. White. Revisions to the JDL data fusion model. In: Proceeding of SPIE Sensor Fusion: Architectures, Algorithms, and Applications III pp. 430-41. 1999.

[21] P. G. Gonsalves., R. Cunningham., N. Ton and D. Okon, Intelligent threat assessment processor (ITAP) using genetic algorithms and fuzzy logic, In: Proc. Internationan Conference on Information Fusion. 2000).

[22] P. Hannah, A. Ball and A. Starr, Decisions in Condition Monitoring - An Examplar for data fusion Architecture. In: Proc. International Conference on Information Fusion, 2000.

[23] Dasarathy B. V., Information Fusion - what, where, why, when, and how? Information Fusion 2: 75-76. 2001.

[24] I. Bloch and A. Hunter (Eds.), A. Appriou, A. Ayoun, S. Benferhat, P. Besnard, L. Cholvy, R. Cooke, F. Cuppens, D. Dubois, H. Fargier, M. Grabisch, R. Kruse, J. Lang, S. Moral, H. Prade, A. Saffiotti, P. Smets, C. Sossai, Fusion:General Concepts and Characteristics, International Journal of Intelligent Systems, 16:1107-1134, 2001.

[25] S. C. McGirr, Resources for the design of data fusion systems, In: Proc. International Conference on Information Fusion, 2001.

[26] B. Bell, E. Santos and S. M. Brown, Making adversary decision modeling tractable with intent inference and information fusion, In: Proc. of the 11th conf on computer generated forces and behavioural representation. 2002.

[27] S.Challa, T. Gulrez, Z. Chaczko and T. N. Paranesha. Opportunistic information fusion: A new paradigm for next generation networked sensing systems. In: Proc. International Conference on Information Fusion. 2005.

[28] A. Jalobeanu, J.A. Gutirrez: Multisource data fusion for bandlimited signals: A Bayesian perspective. In Proc. of 25th workshop on Bayesian Inference and Maximum Entropy methods (MaxEnt’06), Paris, France, Aug 2006.

[29] F. Mastrogiovanni, A. Sgorbissa and R. Zaccaria. A Distributed Architecture for Symbolic Data Fusion. In IJCAI-07, pp 2153-2158. 2007.

[30] Wikipedia. Information Fusion. URL: http:/ Fusion. [accessed Februrary 13, 2007] .

[31] Wikipedia. Sensor Fusion. URL: fusion. [accessed Februrary 13, 2007] .[32] MSN Encarta. Data fusion definition. URL: 701705479/data fusion.html [accessed Februrary 21, 2007] .