An introduction to multi-sensor data fusion

Multi-sensor data fusion is an emerging technology applied to Department of Defense (DoD) areas such as automated target recognition, battlefield surveillance, and guidance and control of autonomous vehicles, and to non-DoD applications such as monitoring of complex machinery, medical diagnosis, and smart buildings. Techniques for multi-sensor data fusion are drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and other areas. This paper provides a tutorial on data fusion, introducing data fusion applications, process models, and identification of applicable techniques. Comments are made on the state-of-the-art in data fusion.

[1]  Lawrence A. Klein,et al.  Sensor and Data Fusion Concepts and Applications , 1993 .

[2]  Raymond Kurzweil,et al.  Age of intelligent machines , 1990 .

[3]  David A. Landgrebe,et al.  Decision boundary feature extraction for nonparametric classification , 1993, IEEE Trans. Syst. Man Cybern..

[4]  P. L. Rothman,et al.  Evaluation of sensor management systems , 1989, Proceedings of the IEEE National Aerospace and Electronics Conference.

[5]  J. Cappellari,et al.  Mathematical theory of the Goddard trajectory determination system , 1976 .

[6]  H. Sorenson Least-squares estimation: from Gauss to Kalman , 1970, IEEE Spectrum.

[7]  Keung-Chi Ng,et al.  Uncertainty management in expert systems , 1990, IEEE Expert.

[8]  Stelios C. A. Thomopoulos,et al.  Distributed Fusion Architectures and Algorithms for Target Tracking , 1997, Proc. IEEE.

[9]  Y. Bar-Shalom,et al.  Tracking in a cluttered environment with probabilistic data association , 1975, Autom..

[10]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[11]  M. Bedworth,et al.  The Omnibus model: a new model of data fusion? , 2000 .

[12]  P. A. Delaney,et al.  A bibliography of higher-order spectra and cumulants , 1994 .

[13]  Gregory A. McIntyre,et al.  A Comprehensive Approach to Sensor Management and Scheduling , 1998 .

[14]  Derek C. Lang,et al.  The Negative Information Problem in Mechanical Diagnostics , 1997 .

[15]  Richard E. Neapolitan,et al.  Probabilistic reasoning in expert systems - theory and algorithms , 2012 .

[16]  Mongi A. Abidi,et al.  Data fusion in robotics and machine intelligence , 1992 .

[17]  Andrew P. Sage,et al.  An overview of automated reasoning , 1990, IEEE Trans. Syst. Man Cybern..

[18]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[19]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[20]  H. Skinner Dimensions and Clusters: A Hybrid Approach to Classification , 1979 .

[21]  J.R. Cloutier,et al.  Spherical target state estimators , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[22]  Bernard Widrow,et al.  Neural nets for adaptive filtering and adaptive pattern recognition , 1988, Computer.

[23]  D. L. Hall,et al.  A new approach to the challenge of machinery prognostics , 1995 .

[24]  Peter Jackson,et al.  Introduction to expert systems , 1986 .

[25]  James Llinas,et al.  Survey of multisensor data fusion systems , 1991, Defense, Security, and Sensing.

[26]  Roy L. Streit,et al.  Maximum likelihood method for probabilistic multihypothesis tracking , 1994, Defense, Security, and Sensing.

[27]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[28]  James Llinas,et al.  Multisensor Data Fusion , 1990 .

[29]  Andreas S. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[30]  T. Kailath,et al.  A state-space approach to adaptive RLS filtering , 1994, IEEE Signal Processing Magazine.